Dr. Anthony Chang

“A room without books is like a body without a soul.”

                                                                                                                                       Cicero

One of the questions I get asked most often in the many meetings that I am privileged to be part of on AI in healthcare is, "how do I (as a student, trainee, nurse, or senior executive in healthcare) pursue my nascent interest of artificial intelligence in health now that I am better informed?"

The following series of notes delineates a suggested and recommended (the latter a stronger sentiment) educational opportunities for anyone interested in AI in clinical medicine and healthcare at all levels of education and all stages of his/her career. This guide can accommodate everyone from the high school student who yearns to be in healthcare to the chief executive of a health system or dean of a health professional school who is curious about artificial intelligence in medicine, health, and healthcare.

AI Self Education

This is the traditional means to learn on your own, including available published manuscripts and books as well as online videos. Even in 2025, much learning can still be in this genre of traditional educational resources. 

Articles 

Under the American Board of AI in Medicine (ABAIM), we have collected a special portfolio of close to 200 “must read” articles in both AI as well as AI in healthcare. This carefully curated portfolio of papers cover a myriad of topics from machine and deep learning, cognitive computing, and generative and agentic AI in healthcare to neuroscience inspired AI as well as ethics and regulatory aspects of AI in healthcare. The Intelligence-Based Medicine (IBMed) journal has been publishing articles in biomedical and health AI since 2020 with over 300 published manuscripts from over 40 countries that encourage collaborative efforts between clinicians and data scientists.

In addition, the renowned journals such as Lancet, Nature (npj digital medicine), The Lancet Digital Health, British Medical Journal (BMJ), New England Journal of Medicine (NEJM), and Journal of American Medical Association (JAMA)(including JAMA Network Open) all finally have articles on artificial intelligence in medicine and health after an earlier period of relative dormancy. These articles do vary widely in relatability as some of these works reflect current trends of AI in healthcare but others remain as works in sequestered academic centers and therefore lack real-world application potential.

Most if not all subspecialties now have increasing number of publications on both reviews as well as applications of AI in their domain areas. Lastly, many hot topics in AI including AI in healthcare are published in arXiv (pronounced “archive”), the free and open-access repository of preprints that are early versions of research papers prior to peer review. Papers from arXiv are sometimes more insightful (and interesting) than from traditional medical journals that often lack expediency and relevance. Overall, the literature is replete of articles for every level and need, and cohorts can encourage journal and book clubs to review recent publications (see below). Group discussions of articles and books can lead to very productive critical reviews of these works.

Books

There are now many books on AI, as well as AI in healthcare. As some of you know, I have published several books in a planned AI in Medicine and Healthcare book series over a decade: Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine (2020) and the follow up volume Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine and Surgery (2023), with upcoming three additional titles (all due to arrive in early 2026): 1) Intelligence-Based Healthcare: An Essential Guide with Case Studies of Artificial Intelligence for the Healthcare Leader and Provider; 2) Intelligence-Based Pediatric and Neonatal Medicine: Artificial Intelligence and Clinical Cognition in Pediatric and Neonatal Medicine; and 3) Intelligence-Based Primary Care: Artificial Intelligence and Human Cognition for the Primary Care Clinician. Other subspecialties in planning stages of this series include: nursing and other allied health professionals, neuroscience and mental health, surgery, public and global health, etc.

The following are suggestions for books that will be ideal reading for all those interested in AI in healthcare. With so many books on AI and AI in healthcare especially in the past few years, this list is more and more difficult to curate.

First, there is the “bible” of artificial intelligence: Artificial Intelligence: A Modern Approach (4th edition)(2021) by Stuart Russell and Peter Norvig. This recent edition (first edition was published in 1995) of arguably the most influential textbook in artificial intelligence is the standard textbook of AI used in many universities. The book covers a vast portfolio of topics from search, logic, probability, and planning to machine and deep learning as well as ethics. 

Here are my personal ten favorite books on both AI as well as AI in healthcare (note that none are about traditional programming):

• Artificial Intelligence: A Guide for Thinking Humans (2019) by Melanie Mitchell. Melanie Mitchell is an American computer scientist with an interest in artificial intelligence as well as complex systems and cellular automata. She has a special talent for writing about technical topics with the flair of a fiction author. An updated edition of this book with a new preface will be available later this year.
• Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (2019) by Eric Topol. This is a volume by the renowned cardiologist Eric Topol that delineates how AI can be effectively deployed in healthcare without excessive technical details. The book reveals how artificial intelligence can decrease the cost of medicine as it reduces human mortality.
• Artificial Unintelligence: How Computers Misunderstand the World (2019) by Meredith Broussard. An insightful book by the software developer and journalist Meredith Broussard who reveals reasons why we cannot assume technology will be right (“technochauvinism”). Like the aforementioned author Melanie Mitchell, Meredith Boussard is a gifted technology author.
• Introduction to Biomedical Data Science (2019) and its companion No-Code Data Science: Mastering Advanced Analytics, Machine Learning, and Artificial Intelligence (2023) by Dr. Bob Hoyt. These are two practical books from one of the favorite faculty members of the ABAIM courses, Dr. Hoyt, who is an internal medicine physician as well as an accomplished biomedical informatics expert.
• Artificial Intelligence for Improved Patient Outcomes: Principles for Moving Forward with Rigorous Science (2020) by Daniel Byrne. This book has easy-to-understand key concepts of data science written by a senior lecturer and consultant who is a biostatistician and researcher with many years of experience in healthcare. It is also conveniently pocket-sized.
• The Alignment Problem: Machine Learning and Human Values (2021) by Brian Christian. This in-depth examination of the myriad of issues that AI brings, such as biases and blind spots as well as contradictory goals and false expectations, is well written by the researcher and programmer Brian Christian. Almost 5 years since its publication, this book is still totally relevant.
• Digital Transformation: Survive and Thrive in an Era of Mass Extinction (2019) by Thomas Siebel. This masterclass in print by the technology entrepreneur Thomas Siebel reminds us that the digital transformation is an existential moment for any organization in this AI era. The strategic prioritization of technological projects is a key skill for any organizational leader. 
• Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril by the National Academy of Medicine. The edition in the Learning Health System Series offers an academic but readable document for relevant healthcare stakeholders with attention to current challenges and possible solutions to AI in healthcare that will be accommodating of equity and inclusion.
• Taming Silicon Valley: How We Can Ensure That AI Works For Us (2024) by Gary Marcus. An outstanding book by the neuroscientist and deep learning critic Gary Marcus that explains how we need to be vigilant of Big Tech. As one of the most respected and trusted voices in AI, Gary Marcus and this powerful manifesto are essential to listen to and to read.
• The Singularity is Nearer: When We Merge with AI (2024) by Ray Kurzweil. The noted inventor and futurist Ray Kurzweil published this book almost 20 years after the book The Singularity is Near that rocked the futurist world. He has predicted that AI will reach human intelligence by 2029 with the constellation of AI, intelligent machines, and biotechnology.

In addition, here are my top ten books on an “honorable mention” list of excellent books (in chronological order) that I also highly recommend for not only AI but intelligence in medicine and healthcare in general. These books are particularly relevant and interesting for anyone from the premedical student to healthcare executives and deans of professional schools who are interested in AI in healthcare:

• How Doctors Think (2007) by Jerome Groopman. The former Harvard chair of medicine describes how doctors arrive at diagnoses and treatment decisions. He argues correctly that medical errors often originate from cognitive biases, flawed reasoning, and communication breakdowns rather than lack of knowledge. This basic premise is particularly relevant in the age of AI.
• Decoding the Heavens: A 2,000-Year-Old Computer- And the Century-Long Search to Discover Its Secrets (2009) by Jo Marchant. This book by the science journalist tells the intertwined story of the Antikythera mechanism, the oldest analog computer known to man, and the scientists and historians who tried to unlock its secrets for more than a century. Great lessons for us to learn from.  
• The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy (2011) by Sharon McGrayne. The prolific British science writer McGrayne, in brilliant storytelling fashion, elaborates on arguably the most underappreciated statistic and probability concept in medicine.
• The Scientific Sherlock Holmes: Cracking the Case with Science and Forensics (2011) by James O’Brien. O’Brien, a professor in chemistry, studied how Sherlock Holmes and his exemplary methodology of investigation is very relevant for the sciences. Sir Arthur Conan Doyle was in fact a physician, and he modeled Sherlock Holmes after his surgeon-mentor Dr. Joseph Bell.
• The Laws of Medicine: Field Notes from an Uncertain Science (2015) by Siddhartha Mukherjee. The oncologist-researcher Mukherjee authored this very short volume on the laws of uncertainty, imprecision, and incompleteness. This book was written a decade ago but is more timely than ever and should be required reading for both clinicians and data scientists in healthcare.
• The Creativity Code: Art and Innovation in the Age of AI (2019) by Marcus du Sautoy. This is one of the few books that juxtaposes innovation and AI and focuses on creativity domains such as music, art, and literature. This book by the mathematician Marcus du Sautoy positions AI as a co-creator in innovation and also places AI and innovation in a human-centered framework.
• Spies, Lies, and Algorithms: The History and Future of American Intelligence (2022) by Amy Zegart. This is a fascinating look at the AI influence on espionage in both the U.S. and global landscape. The Stanford intelligence expert Amy Zegart provides a rare inside look into American intelligence with many fascinating parallels to healthcare.
• Power and Prediction: The Disruptive Economics of Artificial Intelligence (2022) by Ajay Agrawal et al. The authors, leaders of the Creative Destruction Lab in Toronto, followed up their initial successful book The Prediction Machines with this one. By embedding business and management principles into AI and its enterprises, this is a delightful read to understand the economics of AI.
• Genesis: Artificial Intelligence, Hope, and the Human Spirit (2024) by Eric Schmidt et al. The former Google executive, along with the late Henry Kissinger and business executive Craig Mundie, authored this book that explores both the promise and peril of AI with references to healthcare. The previous work The Age of AI in 2011 similarly explored the social and ethical implications of AI.
• Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing)(2024) by Salman Khan. The founder of the Khan Academy authored this compelling and inspirational read on how to explore AI’s potential in education. We have much to learn from this work for education in healthcare professions.

And one very special book that deserves a place of its own:

• Consilience: The Unity of Knowledge (1998) by Edward Wilson. This provocative book remains prescient in how all branches of knowledge (natural sciences, social sciences, humanities, and the arts) can and should be unified under a single explanatory framework, ultimately grounded in the natural sciences. AI can be the necessary force to engender “consilience”. 

Finally, there is the outstanding MIT Press Essential Knowledge Series on a myriad of top AI topics such as Algorithms, Metadata, Cloud Computing, Machine Learning, Deep Learning, AI Ethics, Computational Thinking, The Internet of Things, Artificial General Intelligence, and my favorite volume Critical Thinking. The Harvard Business Review also has special articles and even series on artificial intelligence that are very useful especially for healthcare leaders as these references are usually embedded with management and leadership elements. The latest volume on Generative AI is very insightful for everyone.

While it is very tempting to use an LLM to summarize and highlight these books, I strongly recommend that you actually spend the time to read these books as just perusing an AI summary is like watching the trailer of an epic movie: you may get the gist but miss all the beauty in the details.

Videos

There are now abundant educational materials in the form of videos on artificial intelligence, machine learning, deep learning, natural language processing, and now generative and agentic AI that are available (and mostly free) on the Internet. Although some videos prior to 2023 seem somewhat outdated, the video AlphaGo (2017), a documentary on DeepMind’s Go AI tool and its defeat of the human Go champion Lee Sedol, remains both interesting and inspiring. In addition, videos of interviews by certain AI “celebrity experts” are usually enlightening: Sam Altman, Gary Marcus, Eric Schmidt, Yann LeCun, Yoshua Bengio, Demis Hassabis, Ilya Sutskever, Fei-Fei Li, Andrew Ng, and Geoffrey Hinton are my ten favorite AI experts for their insights and wisdom. Also, many video series are not well done, but a few video series are outstanding:

• The 3Blue1Brown video series (3blue1brown.com) on neural networks and transformers, about 10-25 minutes in length, have visually stunning animations that are extremely helpful to understand the technical triumphs of these tools
• The StatQuest video series (https://statquest.org) on about 100 statistics, machine learning, and data science topics are relatively short (most are 5-10 minutes in length with a few about 20-30 minutes in length) but are very relatable and informative
• The more recent IBM Technology video series (5-20 minutes in length) on various hot topics on AI ranging from generative vs agentic AI to AI vs human thinking are very timely and enlightening (the ones by Martin Keen are my favorite ones. 

​​​​​​​Other video series worth mentioning include webinars from the JAMA Network and NEJM AI as well as videos from the academic institutions such as MIT and Stanford (although these can get very technical).

In person education 

The education of AI in healthcare for everyone will be a dedicated track at AIMed25 this year.

Artificial Intelligence in Medicine (AIMed) is the longest running meeting (inaugurated in 2013) focused on artificial intelligence in medicine and healthcare. The meeting is usually attended by well over 1,000 clinicians (from over 50 subspecialties) as well as healthcare leaders, data scientists, students and trainees, and entrepreneurs and investors from all over the world. The 3-day meeting covers a broad range of topics directly or indirectly related to artificial intelligence in medicine and healthcare such as generative AI, agentic AI, large language models, cybersecurity, and intelligent extended reality with three special tracks this year: AI in pediatrics and neonatology, AI in health professional education, and AI and mental health of clinicians and patients. Several special features of this meeting of AI in health include: breakfast workshops focused on hot topics (as determined by our attendees as attendees vote on topics they like to hear), afternoon subspecialty breakout sessions (for over 20 different subspecialties or domain areas), abstract competition with scholarships for accepted scholars, and a special one-day American Board of AI in Medicine (ABAIM) course that has become very popular with our attendees. This year, there will be a special Chief AI Officer agenda during AIMed25.

See you there!

ACC