The Coming of Age of Artificial Intelligence in Medicine

By Vimla L. Patel, Edward H. Shortliffe, M. Stefanelli, Peter Szolovits, Michael R. Berthold, Riccardo Bellazzi, Ameen Abu-Hanna. 2008

549 citations recorded by the source corpus.

Summary This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its “adolescence” (Shortliffe EH. The adolescence of AI in medicine: Will the field come of age in the ‘90s? Artificial Intelligence in Medicine 1993; 5:93–106). In this article, the discussants reflect on medical AI research during the subsequent years and attempt to characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, background): “Driven by the increasing availability of massive multimodality data, and developed computational models and algorithms, the role of AI techniques in healthcare has grown rapidly in the past decade [1], [2], [3], [4].”
Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer (2019, unknown): “In summary, these data demonstrate the potential for deep learning to contribute to clinical IVF in the same way as it has contributed to other areas of human health (Patel et al., 2009).”
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