2018/03/27 by Judy Wawira Gichoya, Judy W. Gichoya, Gichoya, Judy W. +6
Computer Science · Medicine · #Artificial Intelligence in Healthcare and Education #COVID-19 diagnosis using AI #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning in Healthcare #cs.CY
paper · pdf · doi:10.48550/arxiv.1803.11244
arxiv created 2018/03/27 · openalex publication_date 2018/03/27 · arxiv updated 2018/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Advances in AI in the last decade have clearly made economists, politicians, journalists, and citizenry in general believe that the machines are coming to take human jobs. We review 'superhuman' AI performance claims in radiology and then provide a self-reflection on our own work in the area in the form of a critical review, a tribute of sorts to McDermotts 1976 paper, asking the field for some self-discipline. Clearly there is an opportunity to replace humans, but there are better opportunities, as we have discovered to fit cognitive abilities of human and non-humans. We performed one of the first studies in radiology to see how human and AI performance can complement and improve each others performance for detecting pneumonia in chest X-rays. We question if there is a practical wisdom or phronesis that we need to demonstrate in AI today as well as in our field. Using this, we articulate what AI as a field has already and probably can in the future learn from Psychology, Cognitive Science, Sociology and Science and Technology Studies.