Machine Learning and the Profession of Medicine.

By Alison M Darcy, A. Louie, L. Roberts. Journal of the American Medical Association (JAMA), 2016

384 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Endocrinology, Diabetes and Metabolism, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/9f86366feecbcfdf6c5be165fcf38c679164cc89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment (2020, background): “ML has the potential to transform the way medicine works [35].”
Artificial intelligence powers digital medicine (2018, background): “Most sophisticated processes that involve some form of prediction generated from a large data set use this type of AI, including image recognition, web-search, speech-to-text language processing, and e-commerce product recommendations.(2) AI is increasingly incorporated into devices that consumers k”
Artificial Intelligence in Cardiovascular Imaging (2019, background): “Within the last few years, techniques for processing data through multilayered networks, the unprecedented increase in accessible datasets, and the emergence of user-friendly software packages to work with the data have meant an AI-driven revolution in health care is becoming realistic (3).”
The ‘Digital Twin’ to enable the vision of precision cardiology (2020, neutral): “81 In other scenarios, human experts, by not practising on the easy problems solved by the machine, may lose the skills that may still be needed when dealing with difficult cases.”
Machine Learning and Natural Language Processing in Mental Health: Systematic Review (2021, positive): “…NLP can help identify people at clinical risk for depression or suicidal ideation, who most likely do not have access to mental health providers and/or a primary care doctor [61]; however, this reduces confidentiality protection and can lead to increased vulnerability in certain populations [21].”
Machine learning in medicine: a practical introduction (2019, background): “Machine learning has the potential to transform the way that medicine works [32], however, increased enthusiasm has hitherto not been met by increased access to training materials aimed at the knowledge and skill sets of medical practitioners.”