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Maccaro A, Stokes K, Statham L, He L, Williams A, Pecchia L, Piaggio D. Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices. J Pers Med 2024; 14:443. [PMID: 38793025 PMCID: PMC11121798 DOI: 10.3390/jpm14050443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 05/26/2024] Open
Abstract
The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.
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Affiliation(s)
- Alessia Maccaro
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Katy Stokes
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Laura Statham
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Lucas He
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Faculty of Engineering, Imperial College, London SW7 1AY, UK
| | - Arthur Williams
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
| | - Leandro Pecchia
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
- Intelligent Technologies for Health and Well-Being: Sustainable Design, Management and Evaluation, Faculty of Engineering, Università Campus Bio-Medico Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy
| | - Davide Piaggio
- Applied Biomedical Signal Processing Intelligent eHealth Lab, School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (A.M.); (K.S.); (L.S.); (L.H.); (A.W.); (L.P.)
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Windecker S, Gilard M, Achenbach S, Cribier A, Delgado V, Deych N, Drossart I, Eltchaninoff H, Fraser AG, Goncalves A, Hindricks G, Holborow R, Kappetein AP, Kilmartin J, Kurucova J, Lüscher TF, Mehran R, O'Connor DB, Perkins M, Samset E, von Bardeleben RS, Weidinger F. Device innovation in cardiovascular medicine: a report from the European Society of Cardiology Cardiovascular Round Table. Eur Heart J 2024; 45:1104-1115. [PMID: 38366821 DOI: 10.1093/eurheartj/ehae069] [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] [Indexed: 02/18/2024] Open
Abstract
Research performed in Europe has driven cardiovascular device innovation. This includes, but is not limited to, percutaneous coronary intervention, cardiac imaging, transcatheter heart valve implantation, and device therapy of cardiac arrhythmias and heart failure. An important part of future medical progress involves the evolution of medical technology and the ongoing development of artificial intelligence and machine learning. There is a need to foster an environment conducive to medical technology development and validation so that Europe can continue to play a major role in device innovation while providing high standards of safety. This paper summarizes viewpoints on the topic of device innovation in cardiovascular medicine at the European Society of Cardiology Cardiovascular Round Table, a strategic forum for high-level dialogue to discuss issues related to the future of cardiovascular health in Europe. Devices are developed and improved through an iterative process throughout their lifecycle. Early feasibility studies demonstrate proof of concept and help to optimize the design of a device. If successful, this should ideally be followed by randomized clinical trials comparing novel devices vs. accepted standards of care when available and the collection of post-market real-world evidence through registries. Unfortunately, standardized procedures for feasibility studies across various device categories have not yet been implemented in Europe. Cardiovascular imaging can be used to diagnose and characterize patients for interventions to improve procedural results and to monitor devices long term after implantation. Randomized clinical trials often use cardiac imaging-based inclusion criteria, while less frequently trials randomize patients to compare the diagnostic or prognostic value of different modalities. Applications using machine learning are increasingly important, but specific regulatory standards and pathways remain in development in both Europe and the USA. Standards are also needed for smart devices and digital technologies that support device-driven biomonitoring. Changes in device regulation introduced by the European Union aim to improve clinical evidence, transparency, and safety, but they may impact the speed of innovation, access, and availability. Device development programmes including dialogue on unmet needs and advice on study designs must be driven by a community of physicians, trialists, patients, regulators, payers, and industry to ensure that patients have access to innovative care.
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Affiliation(s)
- Stephan Windecker
- Department of Cardiology, Bern University Hospital, Inselspital, University of Bern, Freiburgstrasse, CH-3010 Bern, Switzerland
| | - Martine Gilard
- Département de Cardiologie, Hospital La Cavale Blanche, La Cavale Blanche Hospital Boulevard Tanguy Prigent, 29200 Brest, France
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen-Nürnberg, Germany
| | - Alain Cribier
- Department of Cardiology, Inserm U1096, Univ Rouen Normandie, F-76000 Rouen, France
| | - Victoria Delgado
- Department of Cardiology, University Hospital Germans Trias i Pujol, Badalona, Spain
| | - Nataliya Deych
- Regulatory Affairs, Edwards Lifesciences, Nyon, Switzerland
| | | | - Hélène Eltchaninoff
- Department of Cardiology, University Hospital Charles Nicolle, Rouen, France
| | - Alan G Fraser
- Department of Cardiology, University Hospital of Wales, Cardiff, UK
| | - Alexandra Goncalves
- Precision Diagnostics, Philips, Cambridge, MA, USA
- Department of Surgery and Physiology, Faculty of Medicine, University of Porto Medical School, Porto, Portugal
| | - Gerhard Hindricks
- Department of Cardiology, German Heart Center Charite, Berlin, Germany
| | | | | | | | - Jana Kurucova
- Transcatheter Heart Valve Division, Edwards Lifesciences, Nyon, Switzerland
| | - Thomas F Lüscher
- Department of Cardiology, Royal Brompton and Harefield Hospitals and Imperial College and King's College, London, UK
- Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland
| | - Roxana Mehran
- Icahn School of Medicine, Mount Sinai Hospital, New York, NY, USA
| | | | - Mark Perkins
- GE Healthcare Cardiology Solutions, Harrogate, UK
| | - Eigil Samset
- GE Healthcare Cardiology Solutions, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | | | - Franz Weidinger
- 2nd Medical Department with Cardiology and Intensive Care Medicine, Klinik Landstrasse, Vienna, Austria
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Ammann C, Hadler T, Gröschel J, Kolbitsch C, Schulz-Menger J. Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations. Front Cardiovasc Med 2023; 10:1118499. [PMID: 37144061 PMCID: PMC10151814 DOI: 10.3389/fcvm.2023.1118499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
Background Cardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure. Aim The aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification. Methods U-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis. Results All models showed strong correlation to the expert with respect to quantitative clinical parameters (rz ' = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 ± 4.5 ml for basal, 0.9 ± 1.3 ml for midventricular, 0.9 ± 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (≥0.91) among the CNNs. Conclusion Modifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models.
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Affiliation(s)
- Clemens Ammann
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Thomas Hadler
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Jan Gröschel
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Working Group on CMR, Experimental and Clinical Research Center, A cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité — Universitätsmedizin Berlin, Berlin, Germany
- Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany
- Department of Cardiology and Nephrology, HELIOS Hospital Berlin-Buch, Berlin, Germany
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