Requirements and reliability of AI in the medical context |
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| Institution: | 1. Department of Machine Learning, H. Lee. Moffitt Cancer Center, Tampa, FL, USA;2. Health Data Services, H. Lee. Moffitt Cancer Center, Tampa, FL, USA;1. Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India;2. Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India;3. Jamia Hamdard, New Delhi, India;1. Department of Medical Radiation Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan;2. Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan;3. Department of Radiology, Kumamoto University Hospital, Kumamoto, Japan;1. A.O. U. di Modena, Medical Physics Unit, Modena, Italy;2. A.O. U. di Modena, Radiotherapy Unit, Dept. of Oncology, Modena, Italy;3. Ospedale S. Chiara, Radiotherapy Unit, Trento, Italy;1. UCLouvain – Institut de Recherche Expérimentale et Clinique - Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium;2. KU Leuven – Department of Oncology – Laboratory of Experimental Radiotherapy, Leuven, Belgium;3. University Hospitals Leuven, Department of Radiation Oncology, 3000 Leuven, Belgium;1. School of Clinical Sciences, Queensland University of Technology, Gardens Point Campus, 2 George St, Brisbane 4000, QLD, Australia;2. Centre for Biomedical Technologies (CBT), Queensland University of Technology, Brisbane, Queensland, Australia;3. School of IT & Systems, Faculty of Science and Technology, University of Canberra, 11 Kirinari Street, Bruce 2617, ACT, Australia;4. Ultrasound Laboratory Trento, Department of Information Engineering and Computer Science, University of Trento, Italy;5. Department of Surgery (Royal Melbourne Hospital), University of Melbourne, Royal Parade, Parkville 3050, VIC, Australia;6. Outcomes Research Consortium, Cleveland Clinic, Cleveland, OH, USA;7. CSIRO Health and Biosecurity, The Australian eHealth Research Centre, Australia |
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| Abstract: | The digital information age has been a catalyst in creating a renewed interest in Artificial Intelligence (AI) approaches, especially the subclass of computer algorithms that are popularly grouped into Machine Learning (ML). These methods have allowed one to go beyond limited human cognitive ability into understanding the complexity in the high dimensional data. Medical sciences have seen a steady use of these methods but have been slow in adoption to improve patient care. There are some significant impediments that have diluted this effort, which include availability of curated diverse data sets for model building, reliable human-level interpretation of these models, and reliable reproducibility of these methods for routine clinical use. Each of these aspects has several limiting conditions that need to be balanced out, considering the data/model building efforts, clinical implementation, integration cost to translational effort with minimal patient level harm, which may directly impact future clinical adoption. In this review paper, we will assess each aspect of the problem in the context of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general. |
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| Keywords: | Artificial intelligence Machine learning Reliability Medical applications Oncology |
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