If you want to learn about medical AI, you may come across lots of unfamiliar terms: machine learning? deep learning? natural language processing?
Below are some articles geared toward beginners. They could be a good starting point for readers interested in further exploring the field of medical AI.
Articles for Beginners without a Technical/Engineering Background
- Clinical Journal of the American Society of Nephrology
- “Introduction to Artificial Intelligence and Machine Learning in Nephrology,” Girish N. Nadkarni, March 2023
- “Deep Learning in Medicine,” Samuel P. Heilbroner and Riccardo Miotto, March 2023
- “Natural Language Processing Basics,” Naveen Arivazhagan and Tielman T. Van Vleck, March 2023
- “Application of Natural Language Processing in Nephrology Research,” Douglas Farrell and Lili Chan, June 2023
- “Digital Health Transformers and Opportunities for Artificial Intelligence-Enabled Nephrology,” Benjamin Shickel, Tyler J. Loftus, Yuanfang Ren, Parisa Rashidi, Azra Bihorac, and Tezcan Ozrazgat-Baslanti, April 2023
- “Reinforcement Learning for Clinical Applications,” Kia Khezeli, Scott Siegel, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Azra Bihorac, and Parisa Rashidi, April 2023
- IEEE Spectrum, “How Deep Learning Works,” Samuel K. Moore, David Schneider, and Eliza Strickland, September 28, 2021
“Today’s boom in AI is centered around a technique called deep learning, which is powered by artificial neural networks. Here’s a graphical explanation of how these neural networks are structured and trained…” - The Economist, “Non-tech businesses are beginning to use artificial intelligence at scale,” Alexandra Suich Bass, March 31, 2018
“One of AI’s main effects will be a dramatic drop in the cost of making predictions, says Ajay Agrawal of the University of Toronto and co-author of a new book, Prediction Machines. Just as electricity made lighting much more affordable—a given level of lighting now costs around 400 times less than it did in 1800—so AI will make forecasting more affordable, reliable and widely available.” - Harvard Business Review, “How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist,” Kathryn Hume, October 20, 2017
“Having an intuition for how machine learning algorithms work—even in the most general sense—is becoming an important business skill. Machine learning scientists can’t work in a vacuum; business stakeholders should help them identify problems worth solving…” - MIT Technology Review, “The Seven Deadly Sins of AI Predictions,” Rodney Brooks, October 6, 2017
“Mistaken predictions lead to fears of things that are not going to happen, whether it’s the wide-scale destruction of jobs, the Singularity, or the advent of AI that has values different from ours and might try to destroy us. We need to push back on these mistakes. But why are people making them? I see seven common reasons.” - Wired, “The Myth of Superhuman AI,” Kevin Kelly, April 25, 2017
“I’ve heard that in the future computerized AIs will become so much smarter than us that they will take all our jobs and resources, and humans will go extinct. … Yet buried in this scenario of a takeover of superhuman artificial intelligence are five assumptions which, when examined closely, are not based on any evidence.”
Additional Resources for Beginners
- Fundamentals of Machine Learning for Health Care, Coursera
This free course is offered by Standford University through Coursera and covers basic concepts of artificial intelligence and machine learning for medicine. - Primer on AI and Machine Learning — Beginners Level (Non-Technical), Arun Rao, January 12, 2022.
This Substack page has a long list of articles, books, online classes, and newsletters that may be helpful to certain readers.
Resources for Engineers with a Technical/Engineering Background
- Machine Learning (including Deep Learning and Reinforcement Learning) for Engineers — A Technical Primer, Arun Rao, December 2022
This Substack page has a list of textbooks, online classes, and papers about ML ethics and best practices. For people with an engineering background who want to learn about AI, this list of resources may be a helpful starting point. - Machine Learning YouTube Courses, DAIR.AI GitHub
This GitHub page by DAIR.AI lists an assortment of free YouTube courses on machine learning, deep learning, natural language processing, and other topics.