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Meurers T, Otte K, Abu Attieh H, Briki F, Despraz J, Halilovic M, Kaabachi B, Milicevic V, Müller A, Papapostolou G, Wirth FN, Raisaro JL, Prasser F. A quantitative analysis of the use of anonymization in biomedical research. NPJ Digit Med 2025; 8:279. [PMID: 40369095 PMCID: PMC12078711 DOI: 10.1038/s41746-025-01644-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Accepted: 04/16/2025] [Indexed: 05/16/2025] Open
Abstract
Anonymized biomedical data sharing faces several challenges. This systematic review analyzes 1084 PubMed-indexed studies (2018-2022) using anonymized biomedical data to quantify usage trends across geographic, regulatory, and cultural regions to identify effective approaches and inform implementation agendas. We identified a significant yearly increase in such studies with a slope of 2.16 articles per 100,000 when normalized against the total number of PubMed-indexed articles (p = 0.021). Most studies used data from the US, UK, and Australia (78.2%). This trend remained when normalized by country-specific research output. Cross-border sharing was rare (10.5% of studies). We identified twelve common data sources, primarily in the US (seven) and UK (three), including commercial (seven) and public entities (five). The prevalence of anonymization in the US, UK, and Australia suggests their practices could guide broader adoption. Rare cross-border anonymized data sharing and differences between countries with comparable regulations underscore the need for global standards.
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Affiliation(s)
- Thierry Meurers
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Karen Otte
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Hammam Abu Attieh
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Farah Briki
- Biomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Jérémie Despraz
- Biomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Mehmed Halilovic
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Bayrem Kaabachi
- Biomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Vladimir Milicevic
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Armin Müller
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Grigorios Papapostolou
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Nikolaus Wirth
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jean Louis Raisaro
- Biomedical Data Science Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Fabian Prasser
- Health Data Science Center, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
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2
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Chahal CAA, Alahdab F, Asatryan B, Addison D, Aung N, Chung MK, Denaxas S, Dunn J, Hall JL, Pamir N, Slotwiner DJ, Vargas JD, Armoundas AA. Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2025:e004624. [PMID: 40340425 DOI: 10.1161/circgen.124.004624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2025]
Abstract
Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.
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Affiliation(s)
- C Anwar A Chahal
- Center for Inherited Cardiovascular Diseases, WellSpan Health, York, PA (C.A.A.C.)
- Department of Cardiology, Barts Heart Center, London, United Kingdom (C.A.A.C., N.A.)
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (C.A.A.C.)
| | - Fares Alahdab
- Departments of Cardiology & Biomedical Informatics, Biostatistics, and Epidemiology, University of Missouri, Columbia (F.A.)
| | | | - Daniel Addison
- Division of Cardiovascular Medicine, Department of Medicine, Cardio-Oncology Program, The Ohio State University, Columbus. (D.A.)
- Division of Cancer Prevention and Control, Department of Medicine, College of Medicine, The Ohio State University, Columbus. (D.A.)
| | - Nay Aung
- Department of Cardiology, Barts Heart Center, London, United Kingdom (C.A.A.C., N.A.)
- The William Harvey Research Institute, London School of Medicine & Dentistry, Queen Mary University of London, United Kingdom. (N.A.)
- National Institute for Health and Care Research, Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, United Kingdom. (N.A.)
| | - Mina K Chung
- Departments of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute & Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, OH (M.K.C.)
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, United Kingdom (S.D.)
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Department of Biostatistics & Bioinformatics, Duke Clinical Research Institute, Duke University, Durham, NC (J.D.)
| | | | - Nathalie Pamir
- Center for Preventive Cardiology, Knight Cardiovascular Institute, Oregon Health & Science University, Portland (N.P.)
| | - David J Slotwiner
- Hofstra School of Medicine, North Shore-Long Island Jewish Health System, New York, NY (D.J.S.)
| | - Jose D Vargas
- Veterans Affairs Medical Center (J.D.V.)
- Georgetown University, Washington, DC (J.D.V.)
| | - Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston (A.A.A.)
- Broad Institute, Massachusetts Institute of Technology, Cambridge (A.A.A.)
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3
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Cervera de la Cruz P, Shabani M. Conceptualizing fairness in the secondary use of health data for research: A scoping review. Account Res 2025; 32:233-262. [PMID: 37851101 DOI: 10.1080/08989621.2023.2271394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/12/2023] [Indexed: 10/19/2023]
Abstract
With the introduction of the European Health Data Space (EHDS), the secondary use of health data for research purposes is attracting more attention. Secondary health data processing promises to address novel research questions, inform the design of future research and improve healthcare delivery generally. To comply with the existing data protection regulations, the secondary data use must be fair, among other things. However, there is no clear understanding of what fairness means in the context of secondary use of health data for scientific research purposes. In response, we conducted a scoping review of argument-based literature to explore how fairness in the secondary use of health data has been conceptualized. A total of 35 publications were included in the final synthesis after abstract and full-text screening. Using an inductive approach and a thematic analysis, our review has revealed that balancing individual and public interests, reducing power asymmetries, setting conditions for commercial involvement, and implementing benefit sharing are essential to guarantee fair secondary use research. The findings of this review can inform current and future research practices and policy development to adequately address concerns about fairness in the secondary use of health data.
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Affiliation(s)
| | - Mahsa Shabani
- Metamedica, Faculty of Law and Criminology, University of Ghent, Ghent, Belgium
- Law Centre for Health and Life, Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands
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4
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Hu WF, Wu SZ, Qian Q. Data Spaces in Medicine and Health: Technologies, Applications, and Challenges. CHINESE MEDICAL SCIENCES JOURNAL = CHUNG-KUO I HSUEH K'O HSUEH TSA CHIH 2025; 40:18-28. [PMID: 40164516 DOI: 10.24920/004466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Data space, as an innovative data management and sharing model, is emerging in the medical and health sectors. This study expounds on the conceptual connotation of data space and delineates its key technologies, including distributed data storage, standardization and interoperability of data sharing, data security and privacy protection, data analysis and mining, and data space assessment. By analyzing the real-world cases of data spaces within medicine and health, this study compares the similarities and differences across various dimensions such as purpose, architecture, data interoperability, and privacy protection. Meanwhile, data spaces in these fields are challenged by the limited computing resources, the complexities of data integration, and the need for optimized algorithms. Additionally, legal and ethical issues such as unclear data ownership, undefined usage rights, risks associated with privacy protection need to be addressed. The study notes organizational and management difficulties, calling for enhancements in governance framework, data sharing mechanisms, and value assessment systems. In the future, technological innovation, sound regulations, and optimized management will help the development of the medical and health data space. These developments will enable the secure and efficient utilization of data, propelling the medical industry into an era characterized by precision, intelligence, and personalization.
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Affiliation(s)
- Wan-Fei Hu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China
| | - Si-Zhu Wu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China.
| | - Qing Qian
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China.
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5
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Narayan SM, Kohli N, Martin MM. Addressing contemporary threats in anonymised healthcare data using privacy engineering. NPJ Digit Med 2025; 8:145. [PMID: 40050672 PMCID: PMC11885643 DOI: 10.1038/s41746-025-01520-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Accepted: 02/17/2025] [Indexed: 03/09/2025] Open
Abstract
Cyber-attacks on healthcare entities and leaks of personal identifiable information (PII) are a growing threat. However, it is now possible to learn sensitive characteristics of an individual without PII, by combining advances in artificial intelligence, analytics, and online repositories. We discuss privacy threats and privacy engineering solutions, emphasizing the selection of privacy enhancing technologies for various healthcare cases. Future solutions must consider dynamic flows of data throughout their lifecycle.
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Affiliation(s)
- Sanjiv M Narayan
- Stanford University, School of Medicine, Palo Alto, CA, USA.
- Stanford Institute for Computational and Mathematical Engineering, Palo Alto, CA, USA.
- University of California Berkeley, School of Information Science, Berkeley, CA, USA.
| | - Nitin Kohli
- University of California Berkeley, Center for Effective Global Action, Berkeley, CA, USA
| | - Megan M Martin
- University of California Berkeley, School of Information Science, Berkeley, CA, USA
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6
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Marko JGO, Neagu CD, Anand PB. Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review. BMC Med Inform Decis Mak 2025; 25:57. [PMID: 39910518 PMCID: PMC11796235 DOI: 10.1186/s12911-025-02884-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 01/20/2025] [Indexed: 02/07/2025] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented within these systems. This review aims to assess the influence of AI on health and social care among these populations, particularly with regard to issues related to inclusivity and regulatory concerns. METHODS We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six leading databases were searched, and 129 articles were selected for this review in line with predefined eligibility criteria. RESULTS This research revealed disparities in AI outcomes, accessibility, and representation among diverse groups due to biased data sources and a lack of representation in training datasets, which can potentially exacerbate inequalities in care delivery for marginalized communities. CONCLUSION AI development practices, legal frameworks, and policies must be reformulated to ensure that AI is applied in an equitable manner. A holistic approach must be used to address disparities, enforce effective regulations, safeguard privacy, promote inclusion and equity, and emphasize rigorous validation.
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Affiliation(s)
- John Gabriel O Marko
- University of Bradford Facility of Engineering and Digital Technology, Bradford, UK.
| | - Ciprian Daniel Neagu
- University of Bradford Facility of Engineering and Digital Technology, Bradford, UK
| | - P B Anand
- University of Bradford Faculty of Management Law and Social Sciences, Bradford, UK
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7
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Galanty M, Luitse D, Noteboom SH, Croon P, Vlaar AP, Poell T, Sanchez CI, Blanke T, Išgum I. Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool. Sci Rep 2024; 14:31846. [PMID: 39738436 PMCID: PMC11686007 DOI: 10.1038/s41598-024-83218-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 12/12/2024] [Indexed: 01/02/2025] Open
Abstract
Medical datasets are vital for advancing Artificial Intelligence (AI) in healthcare. Yet biases in these datasets on which deep-learning models are trained can compromise reliability. This study investigates biases stemming from dataset-creation practices. Drawing on existing guidelines, we first developed a BEAMRAD tool to assess the documentation of public Magnetic Resonance Imaging (MRI); Color Fundus Photography (CFP), and Electrocardiogram (ECG) datasets. In doing so, we provide an overview of the biases that may emerge due to inadequate dataset documentation. Second, we examine the current state of documentation for public medical images and signal data. Our research reveals that there is substantial variance in the documentation of image and signal datasets, even though guidelines have been developed in medical imaging. This indicates that dataset documentation is subject to individual discretionary decisions. Furthermore, we find that aspects such as hardware and data acquisition details are commonly documented, while information regarding data annotation practices, annotation error quantification, or data limitations are not consistently reported. This risks having considerable implications for the abilities of data users to detect potential sources of bias through these respective aspects and develop reliable and robust models that can be adapted for clinical practice.
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Affiliation(s)
- Maria Galanty
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.
- Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
| | - Dieuwertje Luitse
- Department of Media Studies, Faculty of Humanities, University of Amsterdam, Amsterdam, The Netherlands
| | - Sijm H Noteboom
- Department of Intensive Care, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Philip Croon
- Department of Cardiology, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States
| | - Alexander P Vlaar
- Department of Intensive Care, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Thomas Poell
- Department of Media Studies, Faculty of Humanities, University of Amsterdam, Amsterdam, The Netherlands
| | - Clara I Sanchez
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
| | - Tobias Blanke
- Department of Media Studies, Faculty of Humanities, University of Amsterdam, Amsterdam, The Netherlands
| | - Ivana Išgum
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
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8
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Campbell MH, Greaves NS. SHARE: An ethical framework for equitable data sharing in Caribbean health research. Rev Panam Salud Publica 2024; 48:e97. [PMID: 39687251 PMCID: PMC11648205 DOI: 10.26633/rpsp.2024.97] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 08/07/2024] [Indexed: 12/18/2024] Open
Abstract
Data sharing increasingly underpins collaborative research to address complex regional and global public health problems. Advances in analytic tools, including machine learning, have expanded the potential benefits derived from large global repositories of open data. Participating in open data collaboratives offers opportunities for Caribbean researchers to advance the health of the region's population through shared data-driven science and policy. However, ethical challenges complicate these efforts. Here we discuss fundamental challenges that threaten to impede progress if not strategically addressed, including power dynamics among funders and researchers in high-income countries and Caribbean stakeholders; research and health equity; threats to privacy; and risk of stigma. These challenges may be exacerbated by resource and infrastructure limitations often seen in small island developing states (SIDS) and low- and middle-income countries. We propose a framework for Safeguarding Health And Research data sharing by promoting Equity (SHARE) for Caribbean researchers and communities participating in shared data science. Using the SHARE framework can support regionally relevant and culturally responsive work already underway in the region and further develop capacity for intentional sharing and (re)use of Caribbean health data.
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Affiliation(s)
- Michael H. Campbell
- The University of the West IndiesCave HillBarbadosThe University of the West Indies, Cave Hill, Barbados
| | - Natalie S. Greaves
- The University of the West IndiesCave HillBarbadosThe University of the West Indies, Cave Hill, Barbados
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9
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Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
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10
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Chabilall J, Brown Q, Cengiz N, Moodley K. Data as scientific currency: Challenges experienced by researchers with sharing health data in sub-Saharan Africa. PLOS DIGITAL HEALTH 2024; 3:e0000635. [PMID: 39446843 PMCID: PMC11500889 DOI: 10.1371/journal.pdig.0000635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 09/09/2024] [Indexed: 10/26/2024]
Abstract
Innovative information-sharing techniques and rapid access to stored research data as scientific currency have proved highly beneficial in healthcare and health research. Yet, researchers often experience conflict between data sharing to promote health-related scientific knowledge for the common good and their personal academic advancement. There is a scarcity of studies exploring the perspectives of health researchers in sub-Saharan Africa (SSA) regarding the challenges with data sharing in the context of data-intensive research. The study began with a quantitative survey and research, after which the researchers engaged in a qualitative study. This qualitative cross-sectional baseline study reports on the challenges faced by health researchers, in terms of data sharing. In-depth interviews were conducted via Microsoft Teams between July 2022 and April 2023 with 16 health researchers from 16 different countries across SSA. We employed purposive and snowballing sampling techniques to invite participants via email. The recorded interviews were transcribed, coded and analysed thematically using ATLAS.ti. Five recurrent themes and several subthemes emerged related to (1) individual researcher concerns (fears regarding data sharing, publication and manuscript pressure), (2) structural issues impacting data sharing, (3) recognition in academia (scooping of research data, acknowledgement and research incentives) (4) ethical challenges experienced by health researchers in SSA (confidentiality and informed consent, commercialisation and benefit sharing) and (5) legal lacunae (gaps in laws and regulations). Significant discomfort about data sharing exists amongst health researchers in this sample of respondents from SSA, resulting in a reluctance to share data despite acknowledging the scientific benefits of such sharing. This discomfort is related to the lack of adequate guidelines and governance processes in the context of health research collaborations, both locally and internationally. Consequently, concerns about ethical and legal issues are increasing. Resources are needed in SSA to improve the quality, value and veracity of data-as these are ethical imperatives. Strengthening data governance via robust guidelines, legislation and appropriate data sharing agreements will increase trust amongst health researchers and data donors alike.
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Affiliation(s)
- Jyothi Chabilall
- Business Management, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Qunita Brown
- Division of Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Nezerith Cengiz
- Division of Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Keymanthri Moodley
- Division of Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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11
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Brown Q, Chabilall J, Cengiz N, Moodley K. Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing. PLOS DIGITAL HEALTH 2024; 3:e0000551. [PMID: 39331661 PMCID: PMC11432837 DOI: 10.1371/journal.pdig.0000551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/17/2024] [Indexed: 09/29/2024]
Abstract
Groundbreaking data-sharing techniques and quick access to stored research data from the African continent are highly beneficial to create diverse unbiased datasets to inform digital health technologies and artificial intelligence in healthcare. Yet health researchers in sub-Saharan Africa (SSA) experience individual and collective challenges that render them cautious and even hesitant to share data despite acknowledging the public health benefits of sharing. This qualitative study reports on the perspectives of health researchers regarding strategies to mitigate these challenges. In-depth interviews were conducted via Microsoft Teams with 16 researchers from 16 different countries across SSA between July 2022 and April 2023. Purposive and snowball sampling techniques were used to invite participants via email. Recorded interviews were transcribed, cleaned, coded and managed through Atlas.ti.22. Thematic Analysis was used to analyse the data. Three recurrent themes and several subthemes emerged around strategies to improve governance of data sharing. The main themes identified were (1) Strategies for change at a policy level: guideline development, (2) Strengthening data governance to improve data quality and (3) Reciprocity: towards equitable data sharing. Building trust is central to the promotion of data sharing amongst researchers on the African continent and with global partners. This can be achieved by enhancing research integrity and strengthening micro and macro level governance. Substantial resources are required from funders and governments to enhance data governance practices, to improve data literacy and to enhance data quality. High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. Engagement with multiple stakeholders including researchers and research communities is necessary to establish an equitable data sharing approach based on reciprocity and mutual benefit.
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Affiliation(s)
- Qunita Brown
- Department of Medicine, Division for Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jyothi Chabilall
- Department of Medicine, Division for Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Business Management, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Nezerith Cengiz
- Department of Medicine, Division for Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Keymanthri Moodley
- Department of Medicine, Division for Medical Ethics and Law, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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12
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Liang Y, Chao H, Zhang J, Wang G, Yan P. Unbiasing Fairness Evaluation of Radiology AI Model. META-RADIOLOGY 2024; 2:100084. [PMID: 38947177 PMCID: PMC11210324 DOI: 10.1016/j.metrad.2024.100084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Fairness of artificial intelligence and machine learning models, often caused by imbalanced datasets, has long been a concern. While many efforts aim to minimize model bias, this study suggests that traditional fairness evaluation methods may be biased, highlighting the need for a proper evaluation scheme with multiple evaluation metrics due to varying results under different criteria. Moreover, the limited data size of minority groups introduces significant data uncertainty, which can undermine the judgement of fairness. This paper introduces an innovative evaluation approach that estimates data uncertainty in minority groups through bootstrapping from majority groups for a more objective statistical assessment. Extensive experiments reveal that traditional evaluation methods might have drawn inaccurate conclusions about model fairness. The proposed method delivers an unbiased fairness assessment by adeptly addressing the inherent complications of model evaluation on imbalanced datasets. The results show that such comprehensive evaluation can provide more confidence when adopting those models.
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Affiliation(s)
- Yuxuan Liang
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th, St, Troy, 12180, New York, United States
| | - Hanqing Chao
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th, St, Troy, 12180, New York, United States
| | - Jiajin Zhang
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th, St, Troy, 12180, New York, United States
| | - Ge Wang
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th, St, Troy, 12180, New York, United States
| | - Pingkun Yan
- Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 110 8th, St, Troy, 12180, New York, United States
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13
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Ritoré Á, Jiménez CM, González JL, Rejón-Parrilla JC, Hervás P, Toro E, Parra-Calderón CL, Celi LA, Túnez I, Armengol de la Hoz MÁ. The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain. PLOS DIGITAL HEALTH 2024; 3:e0000599. [PMID: 39283912 PMCID: PMC11404816 DOI: 10.1371/journal.pdig.0000599] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/20/2024]
Affiliation(s)
- Álvaro Ritoré
- Big Data Department, PMC, Fundación Progreso y Salud, Seville, Spain
| | - Claudia M Jiménez
- Big Data Department, PMC, Fundación Progreso y Salud, Seville, Spain
| | | | | | - Pablo Hervás
- Department of Technology Transfer, Fundación Progreso y Salud, Seville, Spain
| | - Esteban Toro
- Department of Information Systems, Fundación Progreso y Salud, Seville, Spain
| | - Carlos Luis Parra-Calderón
- Department of Technological Innovation, Virgen del Rocío University Hospital, Seville, Spain
- Group of Innovation in Biomedical Informatics, Biomedical Engineering and Health Economics, Institute of Biomedicine of Seville, Seville, Spain
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Isaac Túnez
- Department of Biochemistry and Molecular Biology, University of Córdoba, Córdoba, Spain
- Reina Sofía University Hospital, Córdoba, Spain
- Maimónides Institute of Biomedical Research of Córdoba, Córdoba, Spain
- General Secretariat of Public Health and Research, Development and Innovation in Health, Regional Ministry of Health and Consumer Affairs, Regional Government of Andalusia, Seville, Spain
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Rodríguez-Mejías S, Degli-Esposti S, González-García S, Parra-Calderón CL. Toward the European Health Data Space: The IMPaCT-Data secure infrastructure for EHR-based precision medicine research. J Biomed Inform 2024; 156:104670. [PMID: 38880235 DOI: 10.1016/j.jbi.2024.104670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Art. 50 of the proposal for a Regulation on the European Health Data Space (EHDS) states that "health data access bodies shall provide access to electronic health data only through a secure processing environment, with technical and organizational measures and security and interoperability requirements". OBJECTIVE To identify specific security measures that nodes participating in health data spaces shall implement based on the results of the IMPaCT-Data project, whose goal is to facilitate the exchange of electronic health records (EHR) between public entities based in Spain and the secondary use of this information for precision medicine research in compliance with the General Data Protection Regulation (GDPR). DATA AND METHODS This article presents an analysis of 24 out of a list of 72 security measures identified in the Spanish National Security Scheme (ENS) and adopted by members of the federated data infrastructure developed during the IMPaCT-Data project. RESULTS The IMPaCT-Data case helps clarify roles and responsibilities of entities willing to participate in the EHDS by reconciling technical system notions with the legal terminology. Most relevant security measures for Data Space Gatekeepers, Enablers and Prosumers are identified and explained. CONCLUSION The EHDS can only be viable as long as the fiduciary duty of care of public health authorities is preserved; this implies that the secondary use of personal data shall contribute to the public interest and/or to protect the vital interests of the data subjects. This condition can only be met if all nodes participating in a health data space adopt the appropriate organizational and technical security measures necessary to fulfill their role.
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Affiliation(s)
- Silvia Rodríguez-Mejías
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocio University Hospital/CSIC/University of Seville, Avenue Manuel Siurot S/N, Seville, 41013, Spain
| | | | - Sara González-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocio University Hospital/CSIC/University of Seville, Avenue Manuel Siurot S/N, Seville, 41013, Spain
| | - Carlos Luis Parra-Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocio University Hospital/CSIC/University of Seville, Avenue Manuel Siurot S/N, Seville, 41013, Spain
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15
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Cho H, Froelicher D, Dokmai N, Nandi A, Sadhuka S, Hong MM, Berger B. Privacy-Enhancing Technologies in Biomedical Data Science. Annu Rev Biomed Data Sci 2024; 7:317-343. [PMID: 39178425 PMCID: PMC11346580 DOI: 10.1146/annurev-biodatasci-120423-120107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.
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Affiliation(s)
- Hyunghoon Cho
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA;
| | - David Froelicher
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Natnatee Dokmai
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA;
| | - Anupama Nandi
- Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA;
| | - Shuvom Sadhuka
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Matthew M Hong
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA;
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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16
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Cordes A, Bak M, Lyndon M, Hudson M, Fiske A, Celi LA, McLennan S. Competing interests: digital health and indigenous data sovereignty. NPJ Digit Med 2024; 7:178. [PMID: 38965365 PMCID: PMC11224364 DOI: 10.1038/s41746-024-01171-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 06/14/2024] [Indexed: 07/06/2024] Open
Abstract
Digital health is increasingly promoting open health data. Although this open approach promises a number of benefits, it also leads to tensions with Indigenous data sovereignty movements led by Indigenous peoples around the world who are asserting control over the use of health data as a part of self-determination. Digital health has a role in improving access to services and delivering improved health outcomes for Indigenous communities. However, we argue that in order to be effective and ethical, it is essential that the field engages more with Indigenous peoples´ rights and interests. We discuss challenges and possible improvements for data acquisition, management, analysis, and integration as they pertain to the health of Indigenous communities around the world.
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Affiliation(s)
- Ashley Cordes
- Environmental Studies Program and Department of Data Science, University of Oregon, Eugene, OR, USA
| | - Marieke Bak
- Institute of History and Ethics in Medicine, Department of Preclinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Ethics, Law and Humanities, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Mataroria Lyndon
- Centre for Medical and Health Sciences Education, School of Medicine, The University of Auckland, Auckland, New Zealand
| | - Maui Hudson
- Te Kotahi Research Institute, University of Waikato, Hamilton, New Zealand
| | - Amelia Fiske
- Institute of History and Ethics in Medicine, Department of Preclinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Leo Anthony Celi
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Stuart McLennan
- Institute of History and Ethics in Medicine, Department of Preclinical Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
- Institute for Biomedical Ethics, University of Basel, Basel, Switzerland.
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17
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Wen D, Soltan A, Trucco E, Matin RN. From data to diagnosis: skin cancer image datasets for artificial intelligence. Clin Exp Dermatol 2024; 49:675-685. [PMID: 38549552 DOI: 10.1093/ced/llae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/11/2024] [Accepted: 03/25/2024] [Indexed: 06/26/2024]
Abstract
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritized groups in existing datasets are highlighted, which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques that may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.
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Affiliation(s)
- David Wen
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK
| | - Andrew Soltan
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford Cancer and Haematology Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Emanuele Trucco
- VAMPIRE Project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Rubeta N Matin
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Artificial Intelligence Working Party Group, British Association of Dermatologists, London, UK
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18
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Sadeghi S, Hempel L, Rodemund N, Kirsten T. Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV. Sci Rep 2024; 14:11438. [PMID: 38763952 PMCID: PMC11102905 DOI: 10.1038/s41598-024-61380-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/06/2024] [Indexed: 05/21/2024] Open
Abstract
The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.
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Affiliation(s)
- Sina Sadeghi
- Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany.
| | - Lars Hempel
- Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- Faculty Applied Computer and Bio Sciences, Mittweida University of Applied Sciences, Mittweida, Germany
| | - Niklas Rodemund
- Department of Anaesthesiology, Perioperative Medicine and Intensive Care Medicine, Paracelsus Medical University of Salzburg, Salzburg, Austria
| | - Toralf Kirsten
- Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, Leipzig, Germany
- Faculty Applied Computer and Bio Sciences, Mittweida University of Applied Sciences, Mittweida, Germany
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Raycheva R, Kostadinov K, Mitova E, Iskrov G, Stefanov G, Vakevainen M, Elomaa K, Man YS, Gross E, Zschüntzsch J, Röttger R, Stefanov R. Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening. Orphanet J Rare Dis 2024; 19:147. [PMID: 38582900 PMCID: PMC10998425 DOI: 10.1186/s13023-024-03162-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/30/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.
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Affiliation(s)
- Ralitsa Raycheva
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria.
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria.
| | - Kostadin Kostadinov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Elena Mitova
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Iskrov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Georgi Stefanov
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
| | - Merja Vakevainen
- Pfizer Biopharmaceuticals Group, Medical Affairs, Helsinki, Finland
| | | | - Yuen-Sum Man
- Global Medical Affairs Rare Disease, Novo Nordisk Health Care AG, Zurich, Switzerland
| | - Edith Gross
- EURORDIS - Rare Diseases Europe, 96 Rue Didot, Paris, 75014, France
| | - Jana Zschüntzsch
- Department of Neurology, University Medical Center, Göttingen, Germany
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Rumen Stefanov
- Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
- Bulgarian Association for Promotion of Education and Science, Institute for Rare Disease, Plovdiv, Bulgaria
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20
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Guldemond N. What is meant by 'integrated personalized diabetes management': A view into the future and what success should look like. Diabetes Obes Metab 2024; 26 Suppl 1:14-29. [PMID: 38328815 DOI: 10.1111/dom.15476] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 02/09/2024]
Abstract
Integrated personalized diabetes management (IPDM) has emerged as a promising approach to improving outcomes in patients with diabetes mellitus (DM). This care approach emphasizes the integration and coordination of different providers, including physicians, nurses, dietitians, social workers and pharmacists. The goal of IPDM is to provide patients with personalized care that is tailored to their needs. This review addresses the concept of integrated care and the use of technology (including data, software applications and artificial intelligence) as well as managerial, regulatory and financial aspects. The implementation and upscaling of digitally enabled IPDM are discussed, with elaboration of successful practices and related evidence. Finally, recommendations are made. It is concluded that the adoption of digitally enabled IPDM on a global level is inevitable, considering the challenges created by an increasing prevalence of patients with DM and the need for better outcomes and improvement of health system sustainability.
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Affiliation(s)
- Nick Guldemond
- Department of Public Health and Primary Care, Leiden Universitair Medisch Centrum, Leiden, Netherlands
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21
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Gonçalves MB, Nakayama LF, Ferraz D, Faber H, Korot E, Malerbi FK, Regatieri CV, Maia M, Celi LA, Keane PA, Belfort R. Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye (Lond) 2024; 38:426-433. [PMID: 37667028 PMCID: PMC10858054 DOI: 10.1038/s41433-023-02717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/26/2023] [Accepted: 08/25/2023] [Indexed: 09/06/2023] Open
Abstract
This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it's recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.
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Affiliation(s)
- Mariana Batista Gonçalves
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Luis Filipe Nakayama
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil.
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA.
| | - Daniel Ferraz
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Hanna Faber
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Ophthalmology, University of Tuebingen, Tuebingen, Germany
| | - Edward Korot
- Retina Specialists of Michigan, Grand Rapids, MI, USA
- Stanford University Byers Eye Institute Palo Alto, Palo Alto, CA, USA
| | | | | | - Mauricio Maia
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA
- Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, USA
| | - Pearse A Keane
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Rubens Belfort
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
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Nakayama LF, Restrepo D, Matos J, Ribeiro LZ, Malerbi FK, Celi LA, Regatieri CS. BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.23.24301660. [PMID: 38343827 PMCID: PMC10854338 DOI: 10.1101/2024.01.23.24301660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Introduction The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used Dino V2 Base for feature extraction, with 70% training and 30% testing subsets. Support Vector Machines (SVM) and Logistic Regression (LR) were employed with weighted training. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Results BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. Models were trained on BRSET in three prediction tasks: "diabetes diagnosis"; "sex classification"; and "diabetic retinopathy diagnosis". Discussion BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.
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Affiliation(s)
- Luis Filipe Nakayama
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - David Restrepo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Telematics Department, University of Cauca, Popayán, Cauca, Colombia
| | - João Matos
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Faculty of Engineering of University of Porto, Porto, Portugal
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
| | - Fernando Korn Malerbi
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Caio Saito Regatieri
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
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Joosse HJ, Groenestege WT, Vernooij RWM, De Groot MCH, Hoefer IE, van Solinge WW, Kok MB, Haitjema S. Improving acute kidney injury alerts in tertiary care by linking primary care data: An observational cohort using routine care data. Digit Health 2024; 10:20552076241271767. [PMID: 39161342 PMCID: PMC11331570 DOI: 10.1177/20552076241271767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/25/2024] [Indexed: 08/21/2024] Open
Abstract
Objective Acute kidney injury (AKI) is easily missed and underdiagnosed in routine clinical care. Timely AKI management is important to decrease morbidity and mortality risks. We recently implemented an AKI e-alert at the University Medical Center Utrecht, comparing plasma creatinine concentrations with historical creatinine baselines, thereby identifying patients with AKI. This alert is limited to data from tertiary care, and primary care data can increase diagnostic accuracy for AKI. We assessed the added value of linking primary care data to tertiary care data, in terms of timely diagnosis or excluding AKI. Methods With plasma creatinine tests for 84,984 emergency department (ED) visits, we applied the Kidney Disease Improving Global Outcome guidelines in both tertiary care-only data and linked data and compared AKI cases. Results Using linked data, the presence of AKI could be evaluated in an additional 7886 ED visits. Sex- and age-stratified analyses identified the largest added value for women (an increase of 4095 possible diagnoses) and patients ≥60 years (an increase of 5190 possible diagnoses). We observed 398 additional visits where AKI was diagnosed, as well as 185 cases where AKI could be excluded. We observed no overall decrease in time between baseline and AKI diagnosis (28.4 days vs. 28.0 days). For cases where AKI was diagnosed in both data sets, we observed a decrease of 2.8 days after linkage, indicating a timelier diagnosis of AKI. Conclusions Combining primary and tertiary care data improves AKI diagnostic accuracy in routine clinical care and enables timelier AKI diagnosis.
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Affiliation(s)
- Huibert-Jan Joosse
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Wouter Tiel Groenestege
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Robin WM Vernooij
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mark CH De Groot
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Imo E Hoefer
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Wouter W van Solinge
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Maarten B Kok
- Saltro BV, Unilabs Netherlands, Utrecht, The Netherlands
| | - Saskia Haitjema
- Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Obiora OL, Shead DA, Olivier B. Data sharing considerations and practice among health researchers in Africa: A scoping review. Digit Health 2024; 10:20552076241290955. [PMID: 39493630 PMCID: PMC11528796 DOI: 10.1177/20552076241290955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 09/25/2024] [Indexed: 11/05/2024] Open
Abstract
Objective To examine the way African health researchers share data. It summarized the types of data collected, the data sharing platforms, and how the geographical distribution of the African-based health researchers influenced data sharing practices. Ethical, legal, and social aspects were considered. Institutional and government matters such as research support and funding were identified. Methods PubMed, Web of Science, LILAC, African Journal Archive, and Scopus databases were searched. Full-text screening was conducted, and data was extracted using the data extraction tool published in an a priori Joanna Briggs Institute-published protocol. Discrepancies were resolved by consensus. Data were illustrated using a Preferred Reporting Items for Systematic Reviews and Meta-analyses flow diagram, figures, tables, and a narrative text. Results Of the 3869 studies that were identified, 32 studies were included in the final study. There was a spike in the number of published studies from 2015 to 2019 (n = 24, 75.0%), while a decline followed in the number of publications from 2020 to April 2023 (n = 6, 18.8%). Ten of the studies included were from South Africa, five were from Kenya, three each were from Nigeria and Tanzania, two were from Ghana and Sierra Leone respectively, while one each was from Malawi, Ethiopia, Cameroon, Mali, Gambia, Senegal, and Burkina Faso. Negative factors impacting data sharing practices of health researchers in Africa included barriers to individual research capacity, governmental bureaucracy and corruption, legal obstacles, technological problems, prohibitive costs of publication, lack of funding, institutional delays, and ethical issues. Conclusion This review identified how African health researchers undertook data sharing in their countries. It pinpointed how geographical location and the resultant challenges to data distribution both individually and institutionally influenced health researchers' ability to achieve data sharing and publication of their research. It was clear that many parts of Africa are still not participating in research due to the many factors that negatively impact health data sharing in Africa.
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Affiliation(s)
- Oluchukwu Loveth Obiora
- Nelda C. Stark College of Nursing, Texas Woman's University, Houston, Texas, USA
- Wits Cricket Research Hub for Science, Medicine and Rehabilitation, Department of Physiotherapy, School of Therapeutic Sciences,
Faculty of Health Sciences, University of the Witwatersrand,
Johannesburg, South Africa
| | - Dorothy Agnes Shead
- Department of Anatomy, Faculty of Health Sciences,
University of the Witwatersrand,
Johannesburg, South Africa
- The Witwatersrand Centre for Evidence-Based Practice: A Collaborating Centre of the Joanna Briggs Institute, Johannesburg, South Africa
| | - Benita Olivier
- Wits Cricket Research Hub for Science, Medicine and Rehabilitation, Department of Physiotherapy, School of Therapeutic Sciences,
Faculty of Health Sciences, University of the Witwatersrand,
Johannesburg, South Africa
- Department of Sport, Health Sciences and Social Work, Centre for Healthy Living Research, Oxford Institute of Allied Health Research, Oxford Brookes University, Oxford, UK
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Saenz A, Chen E, Marklund H, Rajpurkar P. The MAIDA initiative: establishing a framework for global medical-imaging data sharing. Lancet Digit Health 2024; 6:e6-e8. [PMID: 37977999 DOI: 10.1016/s2589-7500(23)00222-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 09/06/2023] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Affiliation(s)
- Agustina Saenz
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Emma Chen
- John A Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02115, USA
| | - Henrik Marklund
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA 02115, USA.
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Macpherson MS, Hutchinson CE, Horst C, Goh V, Montana G. Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications. Radiol Artif Intell 2023; 5:e230019. [PMID: 38074779 PMCID: PMC10698609 DOI: 10.1148/ryai.230019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 08/24/2023] [Accepted: 09/07/2023] [Indexed: 01/31/2024]
Abstract
Purpose To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets. Materials and Methods This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction. Results Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error. Conclusion The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.
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Affiliation(s)
- Matthew S. Macpherson
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Charles E. Hutchinson
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Carolyn Horst
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Vicky Goh
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
| | - Giovanni Montana
- From the Mathematics Institute (M.S.M.), Warwick Medical School
(C.E.H.), Department of Statistics (G.M.), and Warwick Manufacturing Group
(G.M.), University of Warwick, Coventry CV4 7AL, United Kingdom;
Department of Radiology, University Hospitals Coventry and Warwickshire NHS
Trust, Coventry, United Kingdom (C.E.H.); School of Biomedical Engineering
& Imaging Sciences, King’s College London, London, United Kingdom
(C.H., V.G.); Department of Radiology, Guy’s and St Thomas’ NHS
Foundation Trust, London, United Kingdom (V.G.); and Alan Turing Institute,
London, United Kingdom (G.M.)
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Restrepo D, Quion J, Vásquez-Venegas C, Villanueva C, Anthony Celi L, Nakayama LF. A scoping review of the landscape of health-related open datasets in Latin America. PLOS DIGITAL HEALTH 2023; 2:e0000368. [PMID: 37878549 PMCID: PMC10599518 DOI: 10.1371/journal.pdig.0000368] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/16/2023] [Indexed: 10/27/2023]
Abstract
Artificial intelligence (AI) algorithms have the potential to revolutionize healthcare, but their successful translation into clinical practice has been limited. One crucial factor is the data used to train these algorithms, which must be representative of the population. However, most healthcare databases are derived from high-income countries, leading to non-representative models and potentially exacerbating health inequities. This review focuses on the landscape of health-related open datasets in Latin America, aiming to identify existing datasets, examine data-sharing frameworks, techniques, platforms, and formats, and identify best practices in Latin America. The review found 61 datasets from 23 countries, with the DATASUS dataset from Brazil contributing to the majority of articles. The analysis revealed a dearth of datasets created by the authors themselves, indicating a reliance on existing open datasets. The findings underscore the importance of promoting open data in Latin America. We provide recommendations for enhancing data sharing in the region.
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Affiliation(s)
- David Restrepo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Telematics Department, University of Cauca, Popayán, Cauca, Colombia
| | - Justin Quion
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Constanza Vásquez-Venegas
- Scientific Image Analysis Lab, Integrative Biology Program, Biomedical Sciences Institute (ICBM), Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Cleva Villanueva
- Instituto Politécnico Nacional, Escuela Superior de Medicina, Ciudad de Mexico, Mexico
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Luis Filipe Nakayama
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Nakayama LF, Zago Ribeiro L, de Oliveira JAE, de Matos JCRG, Mitchell WG, Malerbi FK, Celi LA, Regatieri CVS. Fairness and generalizability of OCT normative databases: a comparative analysis. Int J Retina Vitreous 2023; 9:48. [PMID: 37605208 PMCID: PMC10440930 DOI: 10.1186/s40942-023-00459-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 03/26/2023] [Indexed: 08/23/2023] Open
Abstract
PURPOSE In supervised Machine Learning algorithms, labels and reports are important in model development. To provide a normality assessment, the OCT has an in-built normative database that provides a color base scale from the measurement database comparison. This article aims to evaluate and compare normative databases of different OCT machines, analyzing patient demographic, contrast inclusion and exclusion criteria, diversity index, and statistical approach to assess their fairness and generalizability. METHODS Data were retrieved from Cirrus, Avanti, Spectralis, and Triton's FDA-approval and equipment manual. The following variables were compared: number of eyes and patients, inclusion and exclusion criteria, statistical approach, sex, race and ethnicity, age, participant country, and diversity index. RESULTS Avanti OCT has the largest normative database (640 eyes). In every database, the inclusion and exclusion criteria were similar, including adult patients and excluding pathological eyes. Spectralis has the largest White (79.7%) proportionately representation, Cirrus has the largest Asian (24%), and Triton has the largest Black (22%) patient representation. In all databases, the statistical analysis applied was Regression models. The sex diversity index is similar in all datasets, and comparable to the ten most populous contries. Avanti dataset has the highest diversity index in terms of race, followed by Cirrus, Triton, and Spectralis. CONCLUSION In all analyzed databases, the data framework is static, with limited upgrade options and lacking normative databases for new modules. As a result, caution in OCT normality interpretation is warranted. To address these limitations, there is a need for more diverse, representative, and open-access datasets that take into account patient demographics, especially considering the development of supervised Machine Learning algorithms in healthcare.
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Affiliation(s)
- Luis Filipe Nakayama
- Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America.
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, SP, Brazil.
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, SP, Brazil
| | | | - João Carlos Ramos Gonçalves de Matos
- Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America
- University of Porto, Porto, Portugal
| | | | | | - Leo Anthony Celi
- Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America
- Department of Biostatistics, United States of America, Harvard TH Chan School of Public Health, Boston, MA, United States of America
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States of America
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Gallifant J, Zhang J, Whebell S, Quion J, Escobar B, Gichoya J, Herrera K, Jina R, Chidambaram S, Mehndiratta A, Kimera R, Marcelo A, Fernandez-Marcelo PG, Osorio JS, Villanueva C, Nazer L, Dankwa-Mullan I, Celi LA. A new tool for evaluating health equity in academic journals; the Diversity Factor. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0002252. [PMID: 37578942 PMCID: PMC10424852 DOI: 10.1371/journal.pgph.0002252] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
Current methods to evaluate a journal's impact rely on the downstream citation mapping used to generate the Impact Factor. This approach is a fragile metric prone to being skewed by outlier values and does not speak to a researcher's contribution to furthering health outcomes for all populations. Therefore, we propose the implementation of a Diversity Factor to fulfill this need and supplement the current metrics. It is composed of four key elements: dataset properties, author country, author gender and departmental affiliation. Due to the significance of each individual element, they should be assessed independently of each other as opposed to being combined into a simplified score to be optimized. Herein, we discuss the necessity of such metrics, provide a framework to build upon, evaluate the current landscape through the lens of each key element and publish the findings on a freely available website that enables further evaluation. The OpenAlex database was used to extract the metadata of all papers published from 2000 until August 2022, and Natural language processing was used to identify individual elements. Features were then displayed individually on a static dashboard developed using TableauPublic, which is available at www.equitablescience.com. In total, 130,721 papers were identified from 7,462 journals where significant underrepresentation of LMIC and Female authors was demonstrated. These findings are pervasive and show no positive correlation with the Journal's Impact Factor. The systematic collection of the Diversity Factor concept would allow for more detailed analysis, highlight gaps in knowledge, and reflect confidence in the translation of related research. Conversion of this metric to an active pipeline would account for the fact that how we define those most at risk will change over time and quantify responses to particular initiatives. Therefore, continuous measurement of outcomes across groups and those investigating those outcomes will never lose importance. Moving forward, we encourage further revision and improvement by diverse author groups in order to better refine this concept.
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Affiliation(s)
- Jack Gallifant
- Department of Intensive Care, Imperial College London NHS Trust, London, United Kingdom
| | - Joe Zhang
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Stephen Whebell
- Intensive Care Unit, Townsville University Hospital, Townsville, Queensland, Australia
| | - Justin Quion
- University of the East Ramon Magsaysay Memorial Medical Center, Quezon City, Philippines
| | | | - Judy Gichoya
- School of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Karen Herrera
- Faculty of Medicine, Military Hospital, Managua, Nicaragua
| | - Ruxana Jina
- The Epidemiology and Surveillance Section, National Institute for Occupational Health, National Health Laboratory Services, Gauteng Region, South Africa
- The Wits School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Abha Mehndiratta
- Center for Global Development, Washington, DC, United States of America
| | - Richard Kimera
- Department of Information Technology, Faculty of Computing and Informatics, Mbarara University of Science and Technology, Mbarara, Uganda
- Department of Advanced Convergence, Handong Global University, Pohang-si, South Korea
| | - Alvin Marcelo
- University of the Philippines Manila, Manila, Philippines
| | - Portia Grace Fernandez-Marcelo
- Department of Family and Community Medicine, College of Medicine, University of the Philippines Manila, Manila, Philippines
| | | | - Cleva Villanueva
- Instituto Politecnico Nacional, Escuela Superior de Medicina, Mexico City, Mexico
| | - Lama Nazer
- Department of Pharmacy, King Hussein Cancer Center, Amman, Jordan
| | - Irene Dankwa-Mullan
- Merative, & Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, Massachusetts, United States of America
- Department of Health Policy and Management, Milken Institute School of Public Health, George Washington University, Washington, DC, United States of America
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, Massachusetts, United States of America
- Beth Israel Deaconess Medical Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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31
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Nakayama LF, Mitchell WG, Ribeiro LZ, Dychiao RG, Phanphruk W, Celi LA, Kalua K, Santiago APD, Regatieri CVS, Moraes NSB. Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review. BMJ Open Ophthalmol 2023; 8:e001216. [PMID: 37558406 PMCID: PMC10414056 DOI: 10.1136/bmjophth-2022-001216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 07/04/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study's characteristics, fairness and generalisability efforts. METHODS Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones. RESULTS All the article's authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients' sex was described, but none applied a bias control in their models. CONCLUSION The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.
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Affiliation(s)
- Luis Filipe Nakayama
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Ophthalmology, Sao Paulo Federal University, Sao Paulo, Brazil
| | - William Greig Mitchell
- Department of Ophthalmology, The Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, Sao Paulo Federal University, Sao Paulo, Brazil
| | - Robyn Gayle Dychiao
- University of the Philippines Manila College of Medicine, Manila, Philippines
| | | | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Khumbo Kalua
- Department of Ophthalmology, Blantyre Institute for Community Ophthalmology, BICO, Blantyre, Malawi
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Rösler W, Altenbuchinger M, Baeßler B, Beissbarth T, Beutel G, Bock R, von Bubnoff N, Eckardt JN, Foersch S, Loeffler CML, Middeke JM, Mueller ML, Oellerich T, Risse B, Scherag A, Schliemann C, Scholz M, Spang R, Thielscher C, Tsoukakis I, Kather JN. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023; 149:7997-8006. [PMID: 36920563 PMCID: PMC10374829 DOI: 10.1007/s00432-023-04667-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.
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Affiliation(s)
- Wiebke Rösler
- Department for Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Beissbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Gernot Beutel
- Department for Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Robert Bock
- IMMS Institute for Microelectronics and Mechatronics Systems GmbH (NPO), Ilmenau, Germany
| | - Nikolas von Bubnoff
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Jan-Niklas Eckardt
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Chiara M L Loeffler
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | | | - Thomas Oellerich
- Medizinische Klinik 2-Haematology/Oncology, University Hospital, Frankfurt am Main, Germany
| | - Benjamin Risse
- Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Münster, Münster, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | | | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany
| | | | - Ioannis Tsoukakis
- Department of Hematology and Oncology, Sana Klinikum Offenbach, Offenbach, Germany
| | - Jakob Nikolas Kather
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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Doutreligne M, Degremont A, Jachiet PA, Lamer A, Tannier X. Good practices for clinical data warehouse implementation: A case study in France. PLOS DIGITAL HEALTH 2023; 2:e0000298. [PMID: 37410797 PMCID: PMC10325086 DOI: 10.1371/journal.pdig.0000298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Real-world data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern clinical data warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation, and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema, and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multilevel governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multicentric data reuses as well as innovations in routine care.
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Affiliation(s)
- Matthieu Doutreligne
- Mission Data, Haute Autorité de Santé, Saint-Denis, France
- Inria, Soda team, Palaiseau, France
| | | | | | - Antoine Lamer
- Univ. Lille, CHU Lille, ULR 2694—METRICS: Évaluation des Technologies de santé et des Pratiques médicales, Lille, France
- Fédération régionale de recherche en psychiatrie et santé mentale (F2RSM Psy), Hauts-de-France, Saint-André-Lez-Lille, France
| | - Xavier Tannier
- Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, Laboratoire d’informatique médicale et d’ingénierie des connaissances en e-Santé, LIMICS, France
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de Kok JWTM, de la Hoz MÁA, de Jong Y, Brokke V, Elbers PWG, Thoral P, Castillejo A, Trenor T, Castellano JM, Bronchalo AE, Merz TM, Faltys M, van der Horst ICC, Xu M, Celi LA, van Bussel BCT, Borrat X. A guide to sharing open healthcare data under the General Data Protection Regulation. Sci Data 2023; 10:404. [PMID: 37355751 PMCID: PMC10290652 DOI: 10.1038/s41597-023-02256-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/17/2023] [Indexed: 06/26/2023] Open
Abstract
Sharing healthcare data is increasingly essential for developing data-driven improvements in patient care at the Intensive Care Unit (ICU). However, it is also very challenging under the strict privacy legislation of the European Union (EU). Therefore, we explored four successful open ICU healthcare databases to determine how open healthcare data can be shared appropriately in the EU. A questionnaire was constructed based on the Delphi method. Then, follow-up questions were discussed with experts from the four databases. These experts encountered similar challenges and regarded ethical and legal aspects to be the most challenging. Based on the approaches of the databases, expert opinion, and literature research, we outline four distinct approaches to openly sharing healthcare data, each with varying implications regarding data security, ease of use, sustainability, and implementability. Ultimately, we formulate seven recommendations for sharing open healthcare data to guide future initiatives in sharing open healthcare data to improve patient care and advance healthcare.
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Affiliation(s)
- Jip W T M de Kok
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Miguel Á Armengol de la Hoz
- Big Data Department, PMC, Fundacion Progreso y Salud (FPS), Regional Ministry of Health of Andalucia, Seville, Andalucia, Spain
| | | | | | - Paul W G Elbers
- Center for Critical Care Computational Intelligence (C4I), Department of Intensive Care Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Thoral
- Center for Critical Care Computational Intelligence (C4I), Department of Intensive Care Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Jose M Castellano
- Fundación de Investigación HM Hospitales, Grupo HM Hospitales, Madrid, Spain
| | - Alberto E Bronchalo
- Fundación de Investigación HM Hospitales, Grupo HM Hospitales, Madrid, Spain
| | - Tobias M Merz
- Cardiovascular Intensive Care Unit, Auckland City Hospital, Auckland, New Zealand
| | - Martin Faltys
- Department of Intensive Care Medicine, University Hospital, University of Bern, Bern, Switzerland
| | - Iwan C C van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Minnan Xu
- Philips Research North America, Cambridge, MA, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics Harvard T.H, Chan School of Public Health, Boston, Massachusetts, USA
| | - Bas C T van Bussel
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands.
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands.
| | - Xavier Borrat
- Department of Biostatistics Harvard T.H, Chan School of Public Health, Boston, Massachusetts, USA.
- Anaesthesiology and Critical Care Department, Hospital Clinic de Barcelona, Barcelona, Spain.
- Medical Informatics Department, Hospital Clinic de Barcelona, Barcelona, Spain.
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Malerbi FK, Nakayama LF, Gayle Dychiao R, Zago Ribeiro L, Villanueva C, Celi LA, Regatieri CV. Digital Education for the Deployment of Artificial Intelligence in Health Care. J Med Internet Res 2023; 25:e43333. [PMID: 37347537 PMCID: PMC10337407 DOI: 10.2196/43333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 01/19/2023] [Accepted: 04/05/2023] [Indexed: 06/23/2023] Open
Abstract
Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.
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Affiliation(s)
| | - Luis Filipe Nakayama
- Ophthalmology Department, Sao Paulo Federal University, Sao Paulo, Brazil
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | | | - Lucas Zago Ribeiro
- Ophthalmology Department, Sao Paulo Federal University, Sao Paulo, Brazil
| | - Cleva Villanueva
- Escuela Superior de Medicina, Instituto Politecnico Nacional, Mexico City, Mexico
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States
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Nakayama LF, de Matos JCRG, Stewart IU, Mitchell WG, Martinez-Martin N, Regatieri CVS, Celi LA. Retinal Scans and Data Sharing: The Privacy and Scientific Development Equilibrium. MAYO CLINIC PROCEEDINGS. DIGITAL HEALTH 2023; 1:67-74. [PMID: 40206726 PMCID: PMC11975763 DOI: 10.1016/j.mcpdig.2023.02.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/11/2025]
Abstract
In ophthalmology, extensive use of ancillary imaging has enabled the development of artificial intelligence models, for which data are crucial. A data-sharing environment promotes external validation, collaborative research, and bias assessment before implementation in the real world; however, legal and ethical concerns need to be addressed in this process. The proposed solutions for improving the security of ophthalmic data sharing are patient consent and data-sharing agreements with third parties. Federated learning enables decentralized algorithm development, however, with limited results and unknown risks. Deidentification techniques through image manipulations and synthetically generated images are possible alternatives to improve security. Still, there is no single solution available. The challenge is to determine the appropriate level of risk and ensure accountability for the use of data. Sharing data, including retinal scans, can and should be performed within a trusted research environment, where there are data use agreements and credentialing of researchers, including requirements for training in responsible conduct of data use. In this review, we discuss the challenges and consequences surrounding limited sharing of ophthalmic datasets in the development of digital innovations and explore potential solutions that will enable safer sharing of retinal scan data.
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Affiliation(s)
- Luis Filipe Nakayama
- Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, MA
- Department of Ophthalmology, São Paulo Federal University, Sao Paulo, SP, Brazil
| | | | | | | | - Nicole Martinez-Martin
- Department of Pediatrics, Center for Biomedical Ethics, Stanford School of Medicine, Stanford, CA
- Department of Psychiatry, Center for Biomedical Ethics, Stanford School of Medicine, Stanford, CA
| | | | - Leo Anthony Celi
- Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, MA
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
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Rajpurkar P, Lungren MP. The Current and Future State of AI Interpretation of Medical Images. N Engl J Med 2023; 388:1981-1990. [PMID: 37224199 DOI: 10.1056/nejmra2301725] [Citation(s) in RCA: 153] [Impact Index Per Article: 76.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Pranav Rajpurkar
- From the Department of Biomedical Informatics, Harvard Medical School, Boston (P.R.); the Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, and the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco - both in California (M.P.L.); and Microsoft, Redmond, Washington (M.P.L.)
| | - Matthew P Lungren
- From the Department of Biomedical Informatics, Harvard Medical School, Boston (P.R.); the Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, and the Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco - both in California (M.P.L.); and Microsoft, Redmond, Washington (M.P.L.)
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Kapur SL, Sabatello M. People with disability and privacy in precision medicine research: what's at stake? Trends Genet 2023; 39:335-337. [PMID: 36707316 PMCID: PMC10240978 DOI: 10.1016/j.tig.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 01/26/2023]
Abstract
Re-identification from data used in precision medicine research is presumed to create minimal risk but may disproportionately impact health disparity populations. We consider plausible privacy risks and the negative ramifications thereof for people with disabilities, the largest health disparity population in the USA, and suggest measures to address these concerns.
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Affiliation(s)
- Supriya Lal Kapur
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA; Division of Ethics, Department of Medical Humanities and Ethics, Columbia University Irving Medical Center, New York, NY 10032, USA.
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Strengthening health data governance: new equity and rights-based principles. INTERNATIONAL JOURNAL OF HEALTH GOVERNANCE 2023. [DOI: 10.1108/ijhg-11-2022-0104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
PurposeThis paper introduces a new set of equity and rights-based principles for health data governance (HDG) and makes the case for their adoption into global, regional and national policy and practice.Design/methodology/approachThis paper discusses the need for a unified approach to HDG that maximises the value of data for whole populations. It describes the unique process employed to develop a set of HDG principles. The paper highlights lessons learned from the principle development process and proposes steps to incorporate them into data governance policies and practice.FindingsMore than 200 individuals from 130 organisations contributed to the development of the HDG principles, which are clustered around three interconnected objectives of protecting people, promoting health value and prioritising equity. The principles build on existing norms and guidelines by bringing a human rights and equity lens to HDG.Practical implicationsThe principles offer a strong vision for HDG that reaps the public good benefits of health data whilst safeguarding individual rights. They can be used by governments and other actors as a guide for the equitable collection and use of health data. The inclusive model used to develop the principles can be replicated to strengthen future data governance approaches.Originality/valueThe article describes the first bottom-up effort to develop a set of principles for HDG.
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Artificial intelligence in uveitis: A comprehensive review. Surv Ophthalmol 2023:S0039-6257(23)00044-9. [PMID: 36878360 DOI: 10.1016/j.survophthal.2023.02.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/07/2023]
Abstract
Uveitis is a disease complex characterized by intraocular inflammation of the uvea that is an important cause of blindness and social morbidity. With the dawn of artificial intelligence (AI) and machine learning integration in healthcare, their application in uveitis creates an avenue to improve screening and diagnosis. Our review identified the use of artificial intelligence in studies of uveitis and classified them as diagnosis support, finding detection, screening, and standardization of uveitis nomenclature. The overall performance of models is poor, with limited datasets and a lack of validation studies and publicly available data and codes. We conclude that AI holds great promise to assist with the diagnosis and detection of ocular findings of uveitis, but further studies and large representative datasets are needed to guarantee generalizability and fairness.
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Filicori F, Bitner DP, Fuchs HF, Anvari M, Sankaranaraynan G, Bloom MB, Hashimoto DA, Madani A, Mascagni P, Schlachta CM, Talamini M, Meireles OR. SAGES video acquisition framework-analysis of available OR recording technologies by the SAGES AI task force. Surg Endosc 2023:10.1007/s00464-022-09825-3. [PMID: 36729231 DOI: 10.1007/s00464-022-09825-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/06/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND Surgical video recording provides the opportunity to acquire intraoperative data that can subsequently be used for a variety of quality improvement, research, and educational applications. Various recording devices are available for standard operating room camera systems. Some allow for collateral data acquisition including activities of the OR staff, kinematic measurements (motion of surgical instruments), and recording of the endoscopic video streams. Additional analysis through computer vision (CV), which allows software to understand and perform predictive tasks on images, can allow for automatic phase segmentation, instrument tracking, and derivative performance-geared metrics. With this survey, we summarize available surgical video acquisition technologies and associated performance analysis platforms. METHODS In an effort promoted by the SAGES Artificial Intelligence Task Force, we surveyed the available video recording technology companies. Of thirteen companies approached, nine were interviewed, each over an hour-long video conference. A standard set of 17 questions was administered. Questions spanned from data acquisition capacity, quality, and synchronization of video with other data, availability of analytic tools, privacy, and access. RESULTS Most platforms (89%) store video in full-HD (1080p) resolution at a frame rate of 30 fps. Most (67%) of available platforms store data in a Cloud-based databank as opposed to institutional hard drives. CV powered analysis is featured in some platforms: phase segmentation in 44% platforms, out of body blurring or tool tracking in 33%, and suture time in 11%. Kinematic data are provided by 22% and perfusion imaging in one device. CONCLUSION Video acquisition platforms on the market allow for in depth performance analysis through manual and automated review. Most of these devices will be integrated in upcoming robotic surgical platforms. Platform analytic supplementation, including CV, may allow for more refined performance analysis to surgeons and trainees. Most current AI features are related to phase segmentation, instrument tracking, and video blurring.
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Affiliation(s)
- Filippo Filicori
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, New York, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Daniel P Bitner
- Intraoperative Performance Analytics Laboratory (IPAL), Department of General Surgery, Northwell Health, Lenox Hill Hospital, New York, NY, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Hans F Fuchs
- Department of Surgery, Division of Surgical Robotics and Artificial Intelligence, University of Cologne, Cologne, Germany
| | - Mehran Anvari
- Center for Surgical Invention and Innovation, Department of Surgery, McMaster University, Hamilton, ON, Canada
| | - Ganesh Sankaranaraynan
- Artificial Intelligence and Medical Simulation (AIMS) Lab, Department of Surgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Matthew B Bloom
- Minimally Invasive Surgery Laboratory, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel A Hashimoto
- Department of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Amin Madani
- Surgical Artificial Intelligence Research Academy, Department of Surgery, University Health Network, Toronto, ON, Canada
| | - Pietro Mascagni
- Fondazione Policlinico Universitario A. Gemelli, Rome, Italy
- Institute of Image-Guided Surgery, IHU-Strasbourg, Strasbourg, France
| | - Christopher M Schlachta
- Canadian Surgical Technologies & Advanced Robotics (CSTAR), London Health Sciences Centre, London, ON, Canada
| | - Mark Talamini
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Ozanan R Meireles
- Surgical Artificial Intelligence and Innovation Laboratory (SAIIL), Department of General Surgery, Massachusetts General Hospital, 15 Parkman Street, WAC 339, Boston, MA, 02139, USA.
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Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care. Cancer Treat Rev 2023; 112:102498. [PMID: 36527795 DOI: 10.1016/j.ctrv.2022.102498] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.
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