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Frost EK, Bosward R, Aquino YSJ, Braunack-Mayer A, Carter SM. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. Int J Med Inform 2024; 186:105417. [PMID: 38564959 DOI: 10.1016/j.ijmedinf.2024.105417] [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: 01/03/2024] [Revised: 03/06/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance. MATERIALS AND METHODS We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations. RESULTS Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI. DISCUSSION Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation. CONCLUSION This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.
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Affiliation(s)
- Emma Kellie Frost
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Rebecca Bosward
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Yves Saint James Aquino
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Annette Braunack-Mayer
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
| | - Stacy M Carter
- Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences, and Humanities, University of Wollongong, Australia.
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Gordon ER, Trager MH, Kontos D, Weng C, Geskin LJ, Dugdale LS, Samie FH. Ethical considerations for artificial intelligence in dermatology: a scoping review. Br J Dermatol 2024; 190:789-797. [PMID: 38330217 DOI: 10.1093/bjd/ljae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 02/10/2024]
Abstract
The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.
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Affiliation(s)
- Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Megan H Trager
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Despina Kontos
- University of Pennsylvania, Perelman School of Medicine, Department of Radiology, Philadelphia, PA, USA
- Radiology
| | | | - Larisa J Geskin
- Columbia University Irving Medical Center, Departments of Dermatology
| | - Lydia S Dugdale
- Columbia University Vagelos College of Physicians and Surgeons, Department of Medicine, Center for Clinical Medical Ethics, New York, NY, USA
| | - Faramarz H Samie
- Columbia University Irving Medical Center, Departments of Dermatology
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3
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Wu A, Ngo M, Thomas C. Assessment of patient perceptions of artificial intelligence use in dermatology: A cross-sectional survey. Skin Res Technol 2024; 30:e13656. [PMID: 38481072 PMCID: PMC10938028 DOI: 10.1111/srt.13656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/24/2024] [Accepted: 03/01/2024] [Indexed: 03/17/2024]
Affiliation(s)
- Alexander Wu
- Department of DermatologyUniversity of Texas Southwestern Medical CenterDallasUSA
| | - Madeline Ngo
- Department of DermatologyUniversity of Texas Southwestern Medical CenterDallasUSA
| | - Cristina Thomas
- Department of DermatologyUniversity of Texas Southwestern Medical CenterDallasUSA
- Department of Internal MedicineUniversity of Texas Southwestern Medical CenterDallasUSA
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Karaa S. Impact of direct use of artificial intelligence algorithms on patient autonomy in dermatology. Ann Dermatol Venereol 2024; 151:103245. [PMID: 38422598 DOI: 10.1016/j.annder.2024.103245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/30/2023] [Accepted: 09/27/2023] [Indexed: 03/02/2024]
Affiliation(s)
- S Karaa
- Dermatology Department and University of Paris, Saint-Louis Hospital, Paris, France; Membre du Groupe d'Ethique en Dermatologie.
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Helenason J, Ekström C, Falk M, Papachristou P. Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care - a mixed method study. Scand J Prim Health Care 2024; 42:51-60. [PMID: 37982736 PMCID: PMC10851794 DOI: 10.1080/02813432.2023.2283190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/08/2023] [Indexed: 11/21/2023] Open
Abstract
Objective: Skin examination to detect cutaneous melanomas is commonly performed in primary care. In recent years, clinical decision support systems (CDSS) based on artificial intelligence (AI) have been introduced within several diagnostic fields.Setting: This study employs a variety of qualitative and quantitative methodologies to investigate the feasibility of an AI-based CDSS to detect cutaneous melanoma in primary care.Subjects and Design: Fifteen primary care physicians (PCPs) underwent near-live simulations using the CDSS on a simulated patient, and subsequent individual semi-structured interviews were explored with a hybrid thematic analysis approach. Additionally, twenty-five PCPs performed a reader study (diagnostic assessment on the basis of image interpretation) of 18 dermoscopic images, both with and without help from AI, investigating the value of adding AI support to a PCPs decision. Perceived instrument usability was rated on the System Usability Scale (SUS).Results: From the interviews, the importance of trust in the CDSS emerged as a central concern. Scientific evidence supporting sufficient diagnostic accuracy of the CDSS was expressed as an important factor that could increase trust. Access to AI decision support when evaluating dermoscopic images proved valuable as it formally increased the physician's diagnostic accuracy. A mean SUS score of 84.8, corresponding to 'good' usability, was measured.Conclusion: AI-based CDSS might play an important future role in cutaneous melanoma diagnostics, provided sufficient evidence of diagnostic accuracy and usability supporting its trustworthiness among the users.
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Affiliation(s)
| | | | - Magnus Falk
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Panagiotis Papachristou
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
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Damen LJ, Van Tuyl LHD, Korevaar JC, Knottnerus BJ, De Jong JD. Citizens' perspectives on relocating care: a scoping review. BMC Health Serv Res 2024; 24:202. [PMID: 38355575 PMCID: PMC10868012 DOI: 10.1186/s12913-024-10671-3] [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: 06/16/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Healthcare systems around the world are facing large challenges. There are increasing demands and costs while at the same time a diminishing health workforce. Without reform, healthcare systems are unsustainable. Relocating care, for example, from hospitals to sites closer to patients' homes, is expected to make a key contribution to keeping healthcare sustainable. Given the significant impact of this initiative on citizens, we conducted a scoping review to provide insight into the factors that influence citizens' attitudes towards relocating care. METHOD A scoping review was conducted. The search was performed in the following databases: Pubmed, Embase, Cinahl, and Scopus. Articles had to include relocating healthcare and citizens' perspectives on this topic and the articles had to be about a European country with a strong primary care system. After applying the inclusion and exclusion criteria, 70 articles remained. RESULTS Factors positively influencing citizens' attitudes towards relocating care included: convenience, familiarity, accessibility, patients having more control over their disease, and privacy. Factors influencing negative attitudes included: concerns about the quality of care, familiarity, the lack of physical examination, contact with others, convenience, and privacy. Furthermore, in general, most citizens preferred to relocate care in the studies we found, especially from the hospital to care provided at home. CONCLUSION Several factors influencing the attitude of citizens towards relocating care were found. These factors are very important when determining citizens' preferences for the location of their healthcare. The majority of studies in this review reported that citizens are in favour of relocating care. In general citizens' perspectives on relocating care are very often missing in articles. It was significant that very few studies on relocation from the hospital to the general practitioner were identified.
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Affiliation(s)
- L J Damen
- Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands.
| | - L H D Van Tuyl
- Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands
| | - J C Korevaar
- Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands
- The Hague University of Applied Sciences, The Hague, the Netherlands
| | - B J Knottnerus
- Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands
| | - J D De Jong
- Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands
- CAPHRI, Maastricht University, PO Box 616, 6200 MD Maastricht, Maastricht, the Netherlands
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Grauman Å, Kontro M, Haller K, Nier S, Aakko S, Lang K, Zingaretti C, Meggiolaro E, De Padova S, Marconi G, Martinelli G, Heckman CA, Simonetti G, Bullinger L, Kihlbom U. Personalizing precision medicine: Patients with AML perceptions about treatment decisions. PATIENT EDUCATION AND COUNSELING 2023; 115:107883. [PMID: 37421687 DOI: 10.1016/j.pec.2023.107883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 06/27/2023] [Accepted: 07/03/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND This study aims to explore patients' with acute myeloid leukemia perceptions about precision medicine and their preferences for involvement in this new area of shared decision-making. METHODS Individual semi-structured interviews were conducted in Finland, Italy and Germany (n = 16). The study population included patients aged 24-79 years. Interviews were analyzed with thematic content analysis. RESULTS Patient's perceived lack of knowledge as a barrier for their involvement in decision-making. Treatment decisions were often made rapidly based on the patient's intuition and trust for the physician rather than on information, in situations that decrease the patient's decision capacity. The patients emphasized that they are in a desperate situation that makes them willing to accept treatment with low probabilities of being cured. CONCLUSIONS The study raised important issues regarding patients' understanding of precision medicine and challenges concerning how to involve patients in medical decision-making. Although technical advances were viewed positively, the role of the physician as an expert and person-of-trust cannot be replaced. PRACTICE IMPLICATIONS Regardless of patients' preferences for involvement in decision-making, information plays a crucial role for patients' perceived involvement in their care. The concepts related to precision medicine are complex and will imply challenges to patient education.
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Affiliation(s)
- Åsa Grauman
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden.
| | - Mika Kontro
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Hematology, Helsinki University, Helsinki, Finland; Foundation for the Finnish Cancer Institute, Helsinki, Finland
| | - Karl Haller
- Department of Hematology, Oncology, and Cancer Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Sofia Aakko
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Katharina Lang
- Department of Hematology, Oncology, and Cancer Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Chiara Zingaretti
- Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Elena Meggiolaro
- Psycho-oncology Service, Palliative care, Pain therapy and Integrative Medicine Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Silvia De Padova
- Psycho-oncology Service, Palliative care, Pain therapy and Integrative Medicine Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Giovanni Marconi
- Hematology Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Giovanni Martinelli
- Scientific Directorate, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Giorgia Simonetti
- Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, FC, Italy
| | - Lars Bullinger
- Department of Hematology, Oncology, and Cancer Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ulrik Kihlbom
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden; Stockholm Centre for Health Care Ethics (CHE), LIME, Karoliniska Institutet, Sweden
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8
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Hernández-Rodríguez JC, Durán-López L, Domínguez-Morales JP, Ortiz-Álvarez J, Conejo-Mir J, Pereyra-Rodriguez JJ. Prediction of melanoma Breslow thickness using deep transfer learning algorithms. Clin Exp Dermatol 2023; 48:752-758. [PMID: 36970775 DOI: 10.1093/ced/llad107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 07/20/2023]
Abstract
BACKGROUND The distinction between in situ melanoma (MIS) and invasive melanoma is challenging even for expert dermatologists. The use of pretrained convolutional neural networks (CNNs) as ancillary decision systems needs further research. AIM To develop, validate and compare three deep transfer learning (DTL) algorithms to predict MIS vs. invasive melanoma and melanoma with a Breslow thickness (BT) of < 0.8 mm vs. ≥ 0.8 mm. METHODS A dataset of 1315 dermoscopic images of histopathologically confirmed melanomas was created from Virgen del Rocio University Hospital and open repositories of the International Skin Imaging Collaboration archive and Polesie S et al. (Dermatol Pract Concept 2021; 11:e2021079). The images were labelled as MIS or invasive melanoma and < 0.8 mm or ≥ 0.8 mm of BT. We conducted three trainings, and overall means for receiver operating characteristic (ROC) curves, sensitivity, specificity, positive and negative predictive value, and balanced diagnostic accuracy outcomes were evaluated on the test set with ResNetV2, EfficientNetB6 and InceptionV3. The results of 10 dermatologists were compared with the algorithms. Grad-CAM gradient maps were generated, highlighting relevant areas considered by the CNNs within the images. RESULTS EfficientNetB6 achieved the highest diagnostic accuracy for the comparison between MIS vs. invasive melanoma (61%) and BT < 0.8 mm vs. ≥ 0.8 mm (75%). For the BT comparison, ResNetV2 with an area under the ROC curve of 0.76 and InceptionV3 with an area under the ROC curve of 0.75, outperformed the results obtained by the dermatologist group with an area under the ROC curve of 0.70. CONCLUSION EfficientNetB6 recorded the best prediction results, outperforming the dermatologists for the comparison of 0.8 mm of BT. DTL could be an ancillary aid to support dermatologists' decisions in the near future.
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Affiliation(s)
| | - Lourdes Durán-López
- Robotics and Technology of Computers Laboratory, University of Seville, Seville, Spain
| | | | - Juan Ortiz-Álvarez
- Department of Dermatology, Virgen del Rocio University Hospital, Seville, Spain
| | - Julián Conejo-Mir
- Department of Dermatology, Virgen del Rocio University Hospital, Seville, Spain
- Department of Medicine, Faculty of Medicine
| | - Jose-Juan Pereyra-Rodriguez
- Department of Dermatology, Virgen del Rocio University Hospital, Seville, Spain
- Department of Medicine, Faculty of Medicine
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Frisinger A, Papachristou P. The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study. BMC PRIMARY CARE 2023; 24:67. [PMID: 36907875 PMCID: PMC10008705 DOI: 10.1186/s12875-023-02024-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 03/01/2023] [Indexed: 03/14/2023]
Abstract
BACKGROUND There is a need to accelerate digital transformation in healthcare to meet increasing needs and demands. The accuracy of medical digital diagnosis tools is improving. The introduction of new technology in healthcare can however be challenging and it is unclear how it should be done to reach desired results. The aim of this study was to explore perceptions and experiences of introducing new Information Technology (IT) in a primary healthcare organisation, exemplified with a Clinical Decision Support System (CDSS) for malignant melanoma. METHODS A qualitative interview-based study was performed in Region Stockholm, Sweden, with fifteen medical doctors representing three different organisational levels - primary care physician, primary healthcare centre manager, and regional manager/chief medical officer. In addition, one software provider was included. Interview data were analysed according to content analysis. RESULTS One central theme "Introduction of digital CDSS in primary healthcare requires a multidimensional perspective and handling" along with seven main categories and thirty-three subcategories emerged from the analysis. Digital transformation showed to be key for current healthcare providers to stay relevant and competitive. However, healthcare represents a closed community, very capable but with lack of time, fostered to be sceptical to new why change needs to bring true value and be inspired by people with medical background to motivate the powerful frontline. CONCLUSIONS This qualitative study revealed structured information of what goes wrong and right and what needs to be considered when driving digital change in primary care organisations. The task shows to be complex and the importance of listening to the voice of healthcare is valuable for understanding the conditions that need to be fulfilled when adopting new technology into a healthcare organization. By considering the findings of this study upcoming digital transformations can improve their success-rate. The information may also be used in developing a holistic approach or framework model, adapted to primary health care, that can support and accelerate the needed digitalization in healthcare as such.
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Affiliation(s)
- Ann Frisinger
- Study Programme in Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Panagiotis Papachristou
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, SE-141 83, Stockholm, Sweden.
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Macri R, Roberts SL. The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making. Curr Oncol 2023; 30:2178-2186. [PMID: 36826129 PMCID: PMC9955933 DOI: 10.3390/curroncol30020168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/28/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when using AI in clinical care; however, there is a lack of practical guidance for clinicians on how to approach these conversations and incorporate patient values into clinical decision making. We provide a practical, values-based guide for clinicians to assist in critical reflection and the incorporation of patient values into shared decision making when deciding to use AI in clinical care. Values that are relevant to patients, identified in the literature, include trust, privacy and confidentiality, non-maleficence, safety, accountability, beneficence, autonomy, transparency, compassion, equity, justice, and fairness. The guide offers questions for clinicians to consider when adopting the potential use of AI in their practice; explores illness understanding between the patient and clinician; encourages open dialogue of patient values; reviews all clinically appropriate options; and makes a shared decision of what option best meets the patient's values. The guide can be used for diverse clinical applications of AI.
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Affiliation(s)
- Rosanna Macri
- Department of Bioethics, Sinai Health, Toronto, ON M5G 1X5, Canada
- Joint Centre for Bioethics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 1P8, Canada
- Department of Radiation Oncology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1P5, Canada
- Correspondence:
| | - Shannon L. Roberts
- Project-Specific Bioethics Research Volunteer Student, Hennick Bridgepoint Hospital, Sinai Health, Toronto, ON M4M 2B5, Canada
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Kedar S, Khazanchi D. Neurology education in the era of artificial intelligence. Curr Opin Neurol 2023; 36:51-58. [PMID: 36367213 DOI: 10.1097/wco.0000000000001130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
PURPOSE OF REVIEW The practice of neurology is undergoing a paradigm shift because of advances in the field of data science, artificial intelligence, and machine learning. To ensure a smooth transition, physicians must have the knowledge and competence to apply these technologies in clinical practice. In this review, we describe physician perception and preparedness, as well as current state for clinical applications of artificial intelligence and machine learning in neurology. RECENT FINDINGS Digital health including artificial intelligence-based/machine learning-based technology has made significant inroads into various aspects of healthcare including neurological care. Surveys of physicians and healthcare stakeholders suggests an overall positive perception about the benefits of artificial intelligence/machine learning in clinical practice. This positive perception is tempered by concerns for lack of knowledge and limited opportunities to build competence in artificial intelligence/machine learning technology. Literature about neurologist's perception and preparedness towards artificial intelligence/machine learning-based technology is scant. There are very few opportunities for physicians particularly neurologists to learn about artificial intelligence/machine learning-based technology. SUMMARY Neurologists have not been surveyed about their perception and preparedness to adopt artificial intelligence/machine learning-based technology in clinical practice. We propose development of a practical artificial intelligence/machine learning curriculum to enhance neurologists' competence in these newer technologies.
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Affiliation(s)
- Sachin Kedar
- Department of Ophthalmology
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Deepak Khazanchi
- Department of Information Systems & Quantitative Analysis, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, Nebraska, USA
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