Fred Wu* and Colmenares-Diaz Eduardo
October 20, 2023
DOI : 10.56831/PSEN-03-084
The field of artificial intelligence (AI) in healthcare is rapidly expanding worldwide, with successful clinical applications in orthopedic disease analysis and multidisciplinary practice. Computer vision-assisted image analysis has several U.S. Food and Drug Administration-approved uses. Recent techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, and natural language processing to infer health trajectories from medical notes. Advanced clinical decision support systems that combine genomics and clinomics are also gaining popularity. Machine/deep learning devices have proliferated, especially for data mining and image analysis, but pose significant challenges to the utility of AI in clinical applications. Legal and ethical questions inevitably arise. This paper proposes a training bias model and training principles to address potential harm to patients and adverse effects on society caused by AI.