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Badawy MK, Carrion D, Mahesh M. Medical physicists at the forefront of multidisciplinary AI integration in healthcare. Phys Med 2025; 135:105007. [PMID: 40409215 DOI: 10.1016/j.ejmp.2025.105007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2025] [Revised: 05/01/2025] [Accepted: 05/19/2025] [Indexed: 05/25/2025] Open
Affiliation(s)
- M K Badawy
- Monash Imaging, Monash Health, Clayton, Victoria, Australia; Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, Victoria, Australia.
| | - D Carrion
- Monash Imaging, Monash Health, Clayton, Victoria, Australia
| | - M Mahesh
- The Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Pandav K, Almahfouz Nasser S, Kimball KH, Higgins K, Madabhushi A. Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients. JCO Oncol Pract 2025:OP2400797. [PMID: 40080779 DOI: 10.1200/op-24-00797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 01/08/2025] [Accepted: 01/17/2025] [Indexed: 03/15/2025] Open
Abstract
Much work has been published on artificial intelligence (AI) and oncology, with many focusing on an algorithm perspective. However, very few perspective articles have explicitly discussed the role of AI in oncology from the perspectives of the stakeholders-the clinicians and the patients. In this article, we delve into the opportunities of AI in oncology from the clinician's and patient's lens. From the clinician's perspective, we discuss reducing burnout, enhancing decision making, and leveraging vast data sets to provide evidence-based recommendations, eventually affecting diagnostic accuracy and treatment planning. From the patient's perspective, we discuss AI virtual concierge, which could improve the cancer care journey by facilitating patient education, mental health support, and personalized lifestyle wellness recommendations promoting a holistic approach to care. We aim to highlight the stakeholders' unmet needs and guide institutions to create innovative AI solutions in oncology. By addressing these perspectives, our article aims to bridge the gap between technological research advancements and their real-world AI-focused clinical applications in cancer care. Understanding and prioritizing the needs of the stakeholders will foster the development of impactful AI tools and intentional utilization of such technology, with an aim for clinical implementation and integration into workflows.
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Julkunen P, Fitzpatrick J, Bamidis PD, Caruana CJ, Katashev A, Laitinen T, Onur S, Rahne T. European Federation of Organisations for medical physics (EFOMP) policy statement no 20: The role of medical physicists and medical physics experts in physiological measurement and related therapies. Phys Med 2025; 130:104923. [PMID: 39904650 DOI: 10.1016/j.ejmp.2025.104923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Accepted: 01/30/2025] [Indexed: 02/06/2025] Open
Abstract
The role of medical physics professionals (MPPs) (medical physicists and medical physics experts) in physiological measurement and related therapies (PM&T) applied in e.g., critical care, neurophysiology, neurology, physiology, audiology, and neurotology has long been acknowledged. Given that the exact role and medical specialty fields in which MPP are involved vary between countries, this policy statement aims to provide direction towards improved definition, harmonisation, and development of the role. This policy statement considers the surveyed experiences from several European countries, state-of-the-art of PM&T, and anticipated future developments. We also present an inventory of competences, and associated knowledge and skills expected of MPPs working in these areas.
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Affiliation(s)
- Petro Julkunen
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland; Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
| | - Jessica Fitzpatrick
- Department of Medical Physics & Clinical Engineering, Beaumont Hospital, Dublin, Ireland
| | - Panagiotis D Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Carmel J Caruana
- Medical Physics, Faculty of Health Sciences, University of Malta, Malta
| | - Alexei Katashev
- Institute of Biomedical Engineering and Nanotechnology, Riga Technical University, Riga, Latvia
| | - Tiina Laitinen
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Sander Onur
- Martini Ziekenhuis, Groningen, the Netherlands
| | - Torsten Rahne
- University Medicine Halle, Department of Otorhinolaryngology, Halle (Saale), Germany
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Conroy L, Winter J, Khalifa A, Tsui G, Berlin A, Purdie TG. Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality. Clin Oncol (R Coll Radiol) 2025; 37:103630. [PMID: 39531894 DOI: 10.1016/j.clon.2024.08.005] [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: 12/06/2023] [Revised: 07/31/2024] [Accepted: 08/08/2024] [Indexed: 11/16/2024]
Abstract
Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces challenges. The transition from controlled retrospective environments to real-world clinical settings introduces heightened scrutiny from clinical end users, potentially leading to decreased clinical acceptance. Key considerations for implementing AI RT planning include ensuring the AI model performance aligns with clinical standards, using high-quality training data, and incorporating sufficient data variation through meticulous curation by clinical experts. Beyond technical aspects, factors such as potential biases and the level of trust clinical end users place in AI may present unforeseen obstacles for real-world clinical use. Addressing these challenges requires bridging education and expertise gaps among clinical end users, enabling them to confidently embrace and utilize AI for routine RT planning. By fostering a better understanding of AI capabilities, building trust, and providing comprehensive training, the promises of AI RT planning can be a reality in the clinical setting. This article assesses the current clinical use of AI RT planning and explores challenges and considerations for bridging gaps in knowledge and expertise for AI operationalization, with focus on training data curation, workflow integration, explainability, bias, and domain knowledge. Remaining challenges in clinical implementation of AI RT treatment planning are examined in the context of trust building approaches.
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Affiliation(s)
- L Conroy
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
| | - J Winter
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
| | - A Khalifa
- Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Medical Biophysics, University of Toronto, Princess Maragret Cancer Research Tower, MaRS Centre, 101 College Street, Room 15-701, Toronto, Ontario, M5G 1L7, Canada.
| | - G Tsui
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada.
| | - A Berlin
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada.
| | - T G Purdie
- Radiation Medicine Program, Princess Margaret Cancer Centre, 610 University Avenue, Toronto, Ontario, M5G 2M9, Canada; Techna Insitute, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada; Department of Radiation Oncology, University of Toronto, 149 College Street - Stewart Building Suite 504, Toronto, Ontario, M5T 1P5, Canada; Department of Medical Biophysics, University of Toronto, Princess Maragret Cancer Research Tower, MaRS Centre, 101 College Street, Room 15-701, Toronto, Ontario, M5G 1L7, Canada.
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5
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Maddalo M, Fanizzi A, Lambri N, Loi E, Branchini M, Lorenzon L, Giuliano A, Ubaldi L, Saponaro S, Signoriello M, Fadda F, Belmonte G, Giannelli M, Talamonti C, Iori M, Tangaro S, Massafra R, Mancosu P, Avanzo M. Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice. Phys Med 2024; 127:104834. [PMID: 39437492 DOI: 10.1016/j.ejmp.2024.104834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 09/19/2024] [Accepted: 10/08/2024] [Indexed: 10/25/2024] Open
Abstract
PURPOSE A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community. METHODS A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model). Both models were validated with cross-validation and on unseen data. The population stability index (PSI) was used as diagnostic metric for implementation issues. Performance was compared to reference. Base and robust models were compared in terms of performance and stability (coefficient of variation (CoV) of prediction probabilities). RESULTS PSI detected potential implementation errors in 70 % of models. The dataset exhibited strong imbalance. The average Gmean (i.e. an appropriate metric for imbalance) among all participants was 0.67 ± 0.01, significantly higher than reference Gmean of 0.50 ± 0.04. Robust models performances were slightly worse than base models (p < 0.05). Regarding stability, robust models exhibited lower median CoV on training set only. CONCLUSION AI4MP-Challenge models overperformed the reference, significantly improving the Gmean. Exclusion of less-robust features did not improve model robustness and it should be avoided when confounding effects are absent. Other methods, like harmonization or data augmentation, should be evaluated. This study demonstrated how the collaborative effort to foster knowledge on machine learning among medical physicists, through interactive sessions and exchange of information among participants, can result in improved models.
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Affiliation(s)
- Michele Maddalo
- Medical Physics Department, Azienda Ospedaliero-Universitaria di Parma 43126 Parma, Italy.
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo, II', 70124 Bari, Italy
| | - Nicola Lambri
- Medical Physics Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy; epartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Emiliano Loi
- Fisica Sanitaria, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Marco Branchini
- Fisica Sanitaria, Azienda Socio Sanitaria Territoriale della Valtellina e dell'Alto Lario, 23100, Sondrio, Italy
| | - Leda Lorenzon
- Fisica Sanitaria, Azienda Sanitaria dell'Alto Adige, 39100 Bolzano, Italy
| | - Alessia Giuliano
- Fisica Sanitaria, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy
| | - Leonardo Ubaldi
- Università degli Studi di Firenze, Dip. Scienze Biomediche Sperimentali e Cliniche "Mario Serio", Firenze 50134, Italy; Istituto Nazionale di Fisica Nucleare, Sez. Firenze, Sesto Fiorentino, Firenze, Italy
| | - Sara Saponaro
- Fisica Sanitaria, Azienda Usl Toscana nord ovest, 56121 Lucca, Italy; University of Pisa, Pisa, Italy
| | - Michele Signoriello
- Fisica Sanitaria, Azienda sanitaria universitaria Giuliano Isontina, 34149 Trieste, Italy
| | - Federico Fadda
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo, II', 70124 Bari, Italy
| | - Gina Belmonte
- Fisica Sanitaria, Azienda Usl Toscana nord ovest, 56121 Lucca, Italy
| | - Marco Giannelli
- Fisica Sanitaria, Azienda Ospedaliero-Universitaria Pisana, 56126 Pisa, Italy
| | - Cinzia Talamonti
- Università degli Studi di Firenze, Dip. Scienze Biomediche Sperimentali e Cliniche "Mario Serio", Firenze 50134, Italy; Istituto Nazionale di Fisica Nucleare, Sez. Firenze, Sesto Fiorentino, Firenze, Italy
| | - Mauro Iori
- Medical Physics Department, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70121 Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori 'Giovanni Paolo, II', 70124 Bari, Italy
| | - Pietro Mancosu
- Medical Physics Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081 Aviano, Italy
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Robert C, Meyer P, Séroussi B, Leroy T, Bibault JE. Artificial intelligence and radiotherapy: Evolution or revolution? Cancer Radiother 2024; 28:503-509. [PMID: 39406605 DOI: 10.1016/j.canrad.2024.09.003] [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: 08/27/2024] [Revised: 08/31/2024] [Accepted: 09/01/2024] [Indexed: 11/03/2024]
Abstract
The integration of artificial intelligence, particularly deep learning algorithms, into radiotherapy represents a transformative shift in the field, enhancing accuracy, efficiency, and personalized care. This paper explores the multifaceted impact of artificial intelligence on radiotherapy, the evolution of the roles of radiation oncologists and medical physicists, and the associated practical challenges. The adoption of artificial intelligence promises to revolutionize the profession by automating repetitive tasks, improving diagnostic precision, and enabling adaptive radiotherapy. However, it also introduces significant risks, such as automation bias, verification failures, and the potential erosion of clinical skills. Ethical considerations, such as maintaining patient autonomy and addressing biases in artificial intelligence systems, are critical to ensuring the responsible use of artificial intelligence. Continuous training and development of robust quality assurance programs are required to mitigate these risks and maximize the benefits of artificial intelligence in radiotherapy.
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Affiliation(s)
- Charlotte Robert
- Inserm, U1030 Molecular Radiotherapy and Therapeutic Innovation, Université Paris-Saclay, Gustave-Roussy, Villejuif, France; Department of Radiation Oncology, Gustave-Roussy, Villejuif, France
| | - Philippe Meyer
- Department of Radiotherapy, Institut de Cancérologie Strasbourg Europe (ICANS), Strasbourg, France; Icube, CNRS UMR 7357, team Images, Strasbourg, France
| | - Brigitte Séroussi
- Inserm, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé (Limics), Sorbonne université, université Sorbonne Paris Nord, 75006 Paris, France; Hôpital Tenon, AP-HP, Paris, France; Délégation ministérielle du numérique en santé, ministère de la Santé, Paris, France
| | - Thomas Leroy
- Department of Radiation Oncology, Centre de Cancérologie des Dentellières, Valenciennes, France.
| | - Jean-Emmanuel Bibault
- Department of Radiation Oncology, Hôpital Europeen Georges-Pompidou, AP-HP, Université Paris-Cité, Paris, France; Institut du Cancer Paris Carpem, Université Paris-Cité, AP-HP, Paris, France
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7
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Lambri N, Dei D, Goretti G, Crespi L, Brioso RC, Pelizzoli M, Parabicoli S, Bresolin A, Gallo P, La Fauci F, Lobefalo F, Paganini L, Reggiori G, Loiacono D, Franzese C, Tomatis S, Scorsetti M, Mancosu P. Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans. Phys Imaging Radiat Oncol 2024; 31:100617. [PMID: 39224688 PMCID: PMC11367262 DOI: 10.1016/j.phro.2024.100617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 09/04/2024] Open
Abstract
Background and purpose Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity. Materials and methods Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool's impact was assessed after nine months of clinical use. Results Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization. Conclusions ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.
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Affiliation(s)
- Nicola Lambri
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Damiano Dei
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Giulia Goretti
- IRCCS Humanitas Research Hospital, Quality Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Leonardo Crespi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
- Health Data Science Centre, Human Technopole, 20157 Milan, Italy
| | - Ricardo Coimbra Brioso
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Marco Pelizzoli
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
- Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Milan, Italy
| | - Sara Parabicoli
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
- Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Milan, Italy
| | - Andrea Bresolin
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Pasqualina Gallo
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Francesco La Fauci
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Francesca Lobefalo
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Lucia Paganini
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Giacomo Reggiori
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Daniele Loiacono
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Ciro Franzese
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Stefano Tomatis
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Marta Scorsetti
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Pietro Mancosu
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
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Jones S, Thompson K, Porter B, Shepherd M, Sapkaroski D, Grimshaw A, Hargrave C. Automation and artificial intelligence in radiation therapy treatment planning. J Med Radiat Sci 2024; 71:290-298. [PMID: 37794690 PMCID: PMC11177028 DOI: 10.1002/jmrs.729] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/12/2023] [Indexed: 10/06/2023] Open
Abstract
Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI-based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher-quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety.
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Affiliation(s)
- Scott Jones
- Radiation Oncology Princess Alexandra Hospital Raymond TerraceBrisbaneQueenslandAustralia
| | - Kenton Thompson
- Department of Radiation Therapy ServicesPeter MacCullum Cancer Care CentreMelbourneVictoriaAustralia
| | - Brian Porter
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
| | - Meegan Shepherd
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
- Monash UniversityClaytonVictoriaAustralia
| | - Daniel Sapkaroski
- Department of Radiation Therapy ServicesPeter MacCullum Cancer Care CentreMelbourneVictoriaAustralia
- RMIT UniversityMelbourneVictoriaAustralia
| | | | - Catriona Hargrave
- Radiation Oncology Princess Alexandra Hospital Raymond TerraceBrisbaneQueenslandAustralia
- Queensland University of Technology, Faculty of Health, School of Clinical SciencesBrisbaneQueenslandAustralia
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9
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Bertholet J, Al Hallaq H, Toma-Dasu I, Ingledew PA, Carlson DJ. Medical Physics Training and Education: Learning From the Past and Looking to the Future. Int J Radiat Oncol Biol Phys 2023; 117:1039-1044. [PMID: 37980131 DOI: 10.1016/j.ijrobp.2023.07.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 07/29/2023] [Indexed: 11/20/2023]
Affiliation(s)
- Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Hania Al Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - Iuliana Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Paris Ann Ingledew
- Department of Radiation Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - David J Carlson
- Department of Therapeutic Radiology, Yale University, New Haven, Connecticut.
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10
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Korreman SS, Behrens CP, Hansen VN, Thygesen J, Andersen TL. New technologies from bench to bedside - report from the Nordic association for clinical physics 2023 symposium. Acta Oncol 2023; 62:1157-1160. [PMID: 37916999 DOI: 10.1080/0284186x.2023.2262111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 11/03/2023]
Affiliation(s)
- Stine Sofia Korreman
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Claus Preibisch Behrens
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Vibeke Nordmark Hansen
- Department of Oncology, Copenhagen University Hospital, - Rigshospitalet, Copenhagen, Denmark
| | - Jesper Thygesen
- Department of Clinical Engineering and Procurement, Central Denmark Region, Aarhus Denmark
| | - Thomas Lund Andersen
- Department of Clinical Physiology & Nuclear Medicine, Rigshospitalet, Copenhagen, Denmark
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Zhong J, Huang T, Qiu M, Guan Q, Luo N, Deng Y. A markerless beam's eye view tumor tracking algorithm based on unsupervised deformable registration learning framework of VoxelMorph in medical image with partial data. Phys Med 2023; 105:102510. [PMID: 36535237 DOI: 10.1016/j.ejmp.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 10/18/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To propose an unsupervised deformable registration learning framework-based markerless beam's eye view (BEV) tumor tracking algorithm for the inferior quality megavolt (MV) images with occlusion and deformation. METHODS Quality assurance (QA) plans for thorax phantom were delivered to the linear accelerator with artificially treatment offsets. Electronic portal imaging device (EPID) images (682 in total) and corresponding digitally reconstructed radiograph (DRR) were gathered as the moving and fixed image pairs, which were randomly divided into training and testing set in a ratio of 0.7:0.3 to train a non-rigid registration model with Voxelmorph. Simultaneously, 533 pairs of patient images from 21 lung tumor plans were acquired for tumor tracking investigation to offer quantifiable tumor motion data. Tracking error and image similarity measures were employed to evaluate the algorithm's accuracy. RESULTS The tracking algorithm can handle image registration with non-rigid deformation and losses ranging from 10 % to 80 %. The tracking errors in the phantom study were below 3 mm in about 86.8 % of cases, and below 2 mm in about 80 % of cases. The normalized mutual information (NMI) changes from 1.182 ± 0.024 to 1.198 ± 0.024 (p < 0.005). The patient target had an average translation of 3.784 mm and a maximum comprehensive displacement value of 7.455 mm. NMI of patient images changes from 1.209 ± 0.027 to 1.217 ± 0.026 (p < 0.005), indicating the presence of non-negligible non-rigid deformation. CONCLUSIONS The study provides a robust markerless tumor tracking algorithm for multi-modal, partial data and inferior quality image processing.
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Affiliation(s)
- Jiajian Zhong
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Taiming Huang
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Minmin Qiu
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Qi Guan
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China
| | - Ning Luo
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China.
| | - Yongjin Deng
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, PR China.
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12
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Santos GNM, da Silva HEC, Figueiredo PTDS, Mesquita CRM, Melo NS, Stefani CM, Leite AF. The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2022. [DOI: 10.1007/s40593-022-00324-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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13
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Prediction and classification of VMAT dosimetric accuracy using plan complexity and log-files analysis. Phys Med 2022; 103:76-88. [DOI: 10.1016/j.ejmp.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 09/20/2022] [Accepted: 10/07/2022] [Indexed: 11/18/2022] Open
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14
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15
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Vandewinckele L, Willems S, Lambrecht M, Berkovic P, Maes F, Crijns W. Treatment plan prediction for lung IMRT using deep learning based fluence map generation. Phys Med 2022; 99:44-54. [DOI: 10.1016/j.ejmp.2022.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/09/2022] [Accepted: 05/15/2022] [Indexed: 11/28/2022] Open
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16
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Barragán-Montero A, Bibal A, Dastarac MH, Draguet C, Valdés G, Nguyen D, Willems S, Vandewinckele L, Holmström M, Löfman F, Souris K, Sterpin E, Lee JA. Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Phys Med Biol 2022; 67:10.1088/1361-6560/ac678a. [PMID: 35421855 PMCID: PMC9870296 DOI: 10.1088/1361-6560/ac678a] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/14/2022] [Indexed: 01/26/2023]
Abstract
The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.
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Affiliation(s)
- Ana Barragán-Montero
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Adrien Bibal
- PReCISE, NaDI Institute, Faculty of Computer Science, UNamur and CENTAL, ILC, UCLouvain, Belgium
| | - Margerie Huet Dastarac
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Camille Draguet
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - Gilmer Valdés
- Department of Radiation Oncology, Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, United States of America
| | - Siri Willems
- ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium
| | | | | | | | - Kevin Souris
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - John A Lee
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
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17
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Fiorino C, Rancati T. Artificial intelligence applied to medicine: There is an "elephant in the room". Phys Med 2022; 98:8-10. [PMID: 35462274 DOI: 10.1016/j.ejmp.2022.04.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/09/2022] [Indexed: 11/27/2022] Open
Affiliation(s)
- Claudio Fiorino
- Medical Physics Department, San Raffaele Scientific Institute, Milano Italy.
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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18
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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
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Ng KH, Wong JHD. A clarion call to introduce artificial intelligence (AI) in postgraduate medical physics curriculum. Phys Eng Sci Med 2022; 45:1-2. [PMID: 35006576 DOI: 10.1007/s13246-022-01099-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Jeannie Hsiu Ding Wong
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
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20
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Artificial Intelligence Assists the Construction of Quantitative Model for the High-Quality Development of Modern Enterprises. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:7211790. [PMID: 34868343 PMCID: PMC8639248 DOI: 10.1155/2021/7211790] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/09/2021] [Accepted: 11/01/2021] [Indexed: 01/07/2023]
Abstract
Artificial intelligence companies are different from traditional labor-intensive and capital-intensive companies in that their core competitiveness lies in technology, knowledge, and manpower. Enterprises show the characteristics of a high proportion of intangible assets, strong profitability, and rapid growth. At the same time, there are also the characteristics of high risk and high uncertainty. In addition to the existing value brought by existing profitability, corporate value should also consider the potential value brought by potential profitability. Enterprise value is affected by many factors such as profitability, growth ability, innovation ability, and external environment. Traditional valuation techniques are often utilised to value artificial intelligence businesses in the present market. Traditional valuation methods ignore the dynamics and uncertainties of artificial intelligence enterprise value evaluation, make static and single predictions of future earnings, ignore the value of enterprise management flexibility, and are unable to assess the intrinsic value of artificial intelligence businesses. Based on the projection pursuit method, this paper constructs a modern high-quality development enterprise high-quality development evaluation model, uses real-code accelerated genetic algorithm to optimize the projection objective function, and calculates the best projection direction vector and projection value. The collected sample data can be imported into the evaluation model to calculate the comprehensive evaluation value of the high-quality development of modern high-quality development enterprises and the weights of various indicators included. By comparing the size of the comprehensive evaluation value, each sample can be calculated Evaluation of the level of high-quality development. The results show that the high-quality development level of China's overall economy is on the rise, but the level of development is still low, and there is a large gap between the development level of the eastern region and the central and western regions. Using the systematic generalized moment estimation method, empirically, we analyse the impact of artificial intelligence on the high-quality economic development. The results show that artificial intelligence at the national level and in the central and western regions will significantly promote high-quality economic development, while artificial intelligence in the eastern region has a significant inhibitory effect on high-quality economic development.
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21
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Khamisy-Farah R, Gilbey P, Furstenau LB, Sott MK, Farah R, Viviani M, Bisogni M, Kong JD, Ciliberti R, Bragazzi NL. Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8989. [PMID: 34501581 PMCID: PMC8430694 DOI: 10.3390/ijerph18178989] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/19/2021] [Accepted: 08/21/2021] [Indexed: 12/02/2022]
Abstract
Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances-including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)-which contribute to the generation of an unprecedented amount of data, so-called 'big data'. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing "Coronavirus Disease 2019" (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.
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Affiliation(s)
- Rola Khamisy-Farah
- Clalit Health Services, Haifa & Western Galilee, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel; (R.K.-F.); (P.G.)
| | - Peter Gilbey
- Clalit Health Services, Haifa & Western Galilee, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel; (R.K.-F.); (P.G.)
| | - Leonardo B. Furstenau
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil;
| | | | - Raymond Farah
- Department of Internal Medicine B, Ziv Medical Center, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel;
| | - Maurizio Viviani
- TransHumanGene, MedicaSwiss, 6330 Cham, Switzerland; (M.V.); (M.B.)
| | - Maurizio Bisogni
- TransHumanGene, MedicaSwiss, 6330 Cham, Switzerland; (M.V.); (M.B.)
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
| | - Rosagemma Ciliberti
- Section of History of Medicine and Bioethics, Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy;
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
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22
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Artificial intelligence and the medical physics profession - A Swedish perspective. Phys Med 2021; 88:218-225. [PMID: 34304045 DOI: 10.1016/j.ejmp.2021.07.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 07/06/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession. MATERIALS AND METHODS We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession. RESULTS Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents' knowledge of and workplace preparedness for AI was generally low. CONCLUSIONS From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.
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23
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Santos JC, Wong JHD, Pallath V, Ng KH. The perceptions of medical physicists towards relevance and impact of artificial intelligence. Phys Eng Sci Med 2021; 44:833-841. [PMID: 34283393 DOI: 10.1007/s13246-021-01036-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/13/2021] [Indexed: 01/04/2023]
Abstract
Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.
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Affiliation(s)
- Josilene C Santos
- Department of Nuclear Physics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jeannie Hsiu Ding Wong
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Vinod Pallath
- Medical Education and Research Development Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
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24
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Artificial intelligence for quality assurance in radiotherapy. Cancer Radiother 2021; 25:623-626. [PMID: 34176724 DOI: 10.1016/j.canrad.2021.06.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 11/24/2022]
Abstract
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial intelligence and in particular machine learning algorithms were mentioned in recent reports as promising solutions to reduce or eliminate the patient-specific quality assurance workload. Several teams successfully experienced a virtual patient-specific quality assurance by training a machine learning tool to predict the results. Training data are generally composed of previous treatment plans and associated patient-specific quality assurance results. However, other training data types were recently introduced such as actual positions and velocities of multileaf collimators, metrics of the plan's complexity, and gravity vectors. Different types of machine learning algorithms were investigated (Poisson regression algorithms, convolutional neural networks, support vector classifiers) with sometimes promising results. These tools are being used for treatment units' quality assurance as well, in particular to analyse the results of imaging devices. Most of these reports were feasibility studies. Using machine learning in clinical routines as a tool that could fully replace quality assurance tests conducted by physics teams has yet to be implemented.
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25
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Meyer P, Biston MC, Khamphan C, Marghani T, Mazurier J, Bodez V, Fezzani L, Rigaud PA, Sidorski G, Simon L, Robert C. Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow. Cancer Radiother 2021; 25:617-622. [PMID: 34175222 DOI: 10.1016/j.canrad.2021.06.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 01/19/2023]
Abstract
Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topic in radiotherapy. Today, the first commercial automated treatment planning solutions are available and implemented in a growing number of clinical radiotherapy departments. It should be noted that these various commercial solutions are based on very different methods, implying a daily practice that varies from one center to another. It is likely that this change in planning practices is still in its infancy. Indeed, the rise of artificial intelligence methods, based in particular on deep learning, has recently revived research interest in this subject. The numerous articles currently being published announce a lasting and profound transformation of radiotherapy planning practices in the years to come. From this perspective, an evolution of initial training for clinical teams and the drafting of new quality assurance recommendations is desirable.
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Affiliation(s)
- P Meyer
- Department of radiotherapy, Institut de Cancérologie Strasbourg Europe (ICANS), Strasbourg, France; ICUBE, CNRS UMR 7357, team IMAGES, Strasbourg, France.
| | - M-C Biston
- Department of radiotherapy, Centre Léon Bérard (CLB), Lyon, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France
| | - C Khamphan
- Department of medical physics, Institut du Cancer Avignon-Provence, Avignon, France
| | - T Marghani
- Institut de radiothérapie Amethyst du Sud de l'Oise, Creil, France
| | - J Mazurier
- Centre de radiothérapie Oncorad Garonne, Toulouse, France
| | - V Bodez
- Department of medical physics, Institut du Cancer Avignon-Provence, Avignon, France
| | - L Fezzani
- Institut de radiothérapie Amethyst du Sud de l'Oise, Creil, France
| | - P A Rigaud
- Institut de radiothérapie Amethyst du Sud de l'Oise, Creil, France
| | - G Sidorski
- Centre de radiothérapie Oncorad Garonne, Toulouse, France
| | - L Simon
- Institut Claudius Regaud (ICR), Institut Universitaire du Cancer de Toulouse-Oncopole (IUCT-O), Toulouse, France; Centre de Recherches en Cancérologie de Toulouse (CRCT), Université de Toulouse, INSERM U1037, Toulouse, France
| | - C Robert
- Université Paris-Saclay, Institut Gustave Roussy, INSERM, Radiothérapie Moléculaire et Innovation Thérapeutique, Villejuif, France; Department of Radiotherapy, Gustave Roussy, Villejuif, France
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26
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Zanca F, Avanzo M, Colgan N, Crijns W, Guidi G, Hernandez-Giron I, Kagadis GC, Diaz O, Zaidi H, Russo P, Toma-Dasu I, Kortesniemi M. Focus issue: Artificial intelligence in medical physics. Phys Med 2021; 83:287-291. [PMID: 34004585 DOI: 10.1016/j.ejmp.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- F Zanca
- Palindromo Consulting, Leuven, Belgium
| | - M Avanzo
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Department of Medical Physics, 33081 Aviano, PN, Italy
| | - N Colgan
- School of Physics, National University of Ireland Galway, Galway, Ireland
| | - W Crijns
- Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven and Department of Radiation Oncology, UZ Leuven, Belgium
| | - G Guidi
- Medical Physics, Az. Ospedaliero-Universitaria di Modena, Modena, Italy
| | - I Hernandez-Giron
- Leiden University Medical Center (LUMC), Radiology Department, Division of Image Processing, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - G C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, GR 265 04, Greece
| | - O Diaz
- Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - H Zaidi
- Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, CH-1211 Geneva, Switzerland
| | - P Russo
- Università di Napoli Federico II, Dipartimento di Fisica "Ettore Pancini", I-80126 Naples, Italy
| | - I Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - M Kortesniemi
- HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. Phys Med 2021; 83:25-37. [DOI: 10.1016/j.ejmp.2021.02.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/27/2021] [Accepted: 02/15/2021] [Indexed: 02/06/2023] Open
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