Requirements and reliability of AI in the medical context. (March 2021)

• Record Type: Journal Article
• Authors: Balagurunathan, Yoganand; Mitchell, Ross; El Naqa, Issam
• Is Part Of: Physica medica. Volume 83(2021)
• Page Start: 72
• Page End: 78
• Publication Date: 2021-03
• Subjects: Artificial intelligence -- Machine learning -- Reliability -- Medical applications -- Oncology; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.02.024
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Reviews developments in Machine Learning (ML) and Artificial Intelligence. Focus Translational application of ML Methods in Oncology. Current Impediments for Reliable ad Reproducibility AI Methods. Recommendations for Reliable, Ethical use of AI methods. 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.