AC LITERATURE

Dr. Anthony Chang

“Tell me and I forget. Teach me and I remember. Involve me and I learn.”                                 
Benjamin Franklin

Beyond involving others in your learning process of AI in medicine and healthcare, there is a growing number of opportunities within health systems and coalitions of health systems that will be of benefit for any avid learner.
 

AI in Healthcare Project (Local Health System). One can get involved in a real-world AI in healthcare project at the local hospital in collaboration with the internal healthcare AI team. While one can do projects with publicly available datasets on the Internet, it is still valuable to have real-time, real-world datasets for projects. As most health systems are in need of data science talent and expertise, a collaborative effort between universities and health systems, even virtual, can create a “win-win” situation to advance the AI in healthcare agenda for both individuals and institutions. Rather than studying AI solely as an abstract concept, clinicians, administrators, and trainees gain insight by engaging with live applications embedded in the clinical environment—such as predictive models for patient deterioration, natural language processing tools for clinical documentation, radiology image analysis platforms, or hospital-level analytics for resource management. Exposure to these systems allows learners to appreciate not only the technical principles behind the tools but also the operational, ethical, and human factors that determine their success or failure. Structured reflection—through debrief sessions, quality-improvement meetings, or committee discussions—ensures that these experiences translate into learning opportunities rather than passive technology use. In this way, AI in health systems functions as both a teaching tool and a mirror, revealing the promises and pitfalls of digital transformation, while equipping healthcare professionals at all levels to think critically about how such technologies should be responsibly deployed in patient care.

AI in Healthcare Committee (Local Health System). Some health organizations are forming AI committees that may invite local representatives (students, patients, and community members). This is a valuable experience in which all members will be learning from each other. The membership of the AI committee can include: experts in informatics, ethics, legal, regulatory, and clinical medicine and healthcare as well as trainees and patients/families. The AI committee can be headed by the nascent executive called the chief health artificial intelligence officer, or CHAIO. Although there is no centralized registry tracking hospitals or health systems with formal AI oversight committees, many hospitals and health systems have already formed dedicated AI groups and AI governance committees due to increasing adoption of AI in these healthcare organizations. These committees typically evaluate new AI tools for clinical use, review evidence of effectiveness and safety, discuss ethical and regulatory considerations, and oversee implementation strategies. By engaging in this process, members—from trainees to senior leaders—learn how AI is assessed not just on technical performance but also on clinical relevance, workflow integration, cost-effectiveness, and equity. When coupled with reflective exercises, such as documenting lessons learned or presenting case summaries, committee work transforms from administrative oversight into a living learning studio, where participants continuously expand their understanding of both the promise and limitations of AI in health systems.

 

AI in Healthcare Meetings (National and International). There is an increasing number of national and international meetings with AI in healthcare content, but relatively few are entirely focused on AI in healthcare. The most well known meetings that do concentrate on AI in healthcare are the Artificial Intelligence in Medicine (AIMed) annual meeting that has been ongoing since 2014 and the more recent HIMSS AI in Healthcare Forum. Other such meetings include Machine Learning in Healthcare (MLHC) and AI in Medicine Europe (AIME) that are relatively heavier in data science content and therefore may not be a good fit for some clinicians. Of note, the aforementioned ABAIM courses also have a weekly ABAIM office hour. National and international meetings on artificial intelligence in healthcare provide a unique forum for immersive, multidisciplinary learning that benefits all levels of healthcare professionals. One such gathering is the Alliance of Centers of AI in Medicine (ACAIM) and its pediatric subgroup Pediatric Centers of AI in Medicine. The ACAIM meeting occurs once monthly in a virtual format. Unlike local workshops or institutional committees, these gatherings bring together clinicians, data scientists, policymakers, industry innovators, and ethicists from around the world, creating a global classroom where diverse perspectives converge. Beyond formal content, the networking opportunities at these meetings are invaluable, enabling cross-institutional collaborations, research partnerships, and mentoring relationships that extend long after the event. By providing both cutting-edge scientific updates and opportunities for reflective dialogue, national and international meetings transform into living laboratories of ideas, equipping participants not only with knowledge of AI’s latest advances but also with the collaborative mindset and strategic vision required to responsibly integrate AI into health systems worldwide.

 

AI in Healthcare Courses (Virtual). With all the videos and courses on AI, there remains a relative paucity of virtual or onsite courses in AI in Healthcare. Virtual courses on AI in healthcare serve as highly valuable assets for a broad spectrum of stakeholders seeking structured and credible education in this rapidly evolving field. One long standing course (since 2010) is the monthly course offered by the American Board of Artificial Intelligence in Medicine (ABAIM) at the one-day primer and two-day basic and advanced levels with accompanying educational certifications. The ABAIM, with a balanced number of clinicians, data scientists, and clinician-data scientists, has graduated close to 3,000 attendees from over 50 countries. In addition, Stanford also has an AI in Healthcare Specialization video course under Coursera. There are also now a few online courses from a few institutions (such as Harvard Medical School, MIT, Stanford, and Johns Hopkins) on AI in healthcare that ranges from a few weeks to a few months that are variable in quality (and price). Most of these courses provide certifications but can be quite expensive (at $2,500 or more). Importantly, the virtual format makes these courses scalable and flexible, enabling participants (often very clinicians who are pre-, on-, or post-call during the course) worldwide to engage asynchronously, while fostering interaction through live sessions, case-based discussions, and assessments that reinforce learning. By providing a standardized and comprehensive educational pathway, AI in healthcare courses like ABAIM not only raise the baseline literacy across diverse audiences but also create a shared language that facilitates collaboration across disciplines, institutions, and levels of training.

AI in Healthcare Rotations (Local Health System). An AI in healthcare rotation within a health system can be an invaluable educational experience for residents and fellows, providing them with structured, hands-on exposure to how artificial intelligence is being developed, validated, and implemented in clinical care. Unlike traditional rotations that focus solely on patient management, an AI-focused rotation allows trainees to engage directly with multidisciplinary teams that include clinicians, data scientists, informaticians, and administrators. An AI in healthcare rotation designed for residents and fellows is very rare currently and perhaps this segment of clinicians is the most underserved in AI in healthcare education. There are exceedingly few hospitals that offer residents and fellows a two- or four-week rotation in AI in healthcare with the AI team at the local institution (Rady Children’s Health, UCSF, and Yale). Through participation in activities such as evaluating AI-based decision-support tools, reviewing datasets for quality and bias, and observing workflow integration of predictive analytics, trainees gain a nuanced understanding of both the potential and limitations of these technologies. Case-based learning during the rotation can highlight how AI predictions compare with clinical judgment, fostering critical thinking and awareness of issues like algorithmic bias, explainability, and patient safety. Residents and fellows may also participate in committee meetings where AI adoption is debated, giving them insight into governance, regulatory, and ethical considerations that shape health system decision-making. This AI in healthcare rotation for trainees can involve faculty from outside the institution to provide a well-rounded real-world experience. There is hope that by the end of the decade, there will be dozens if not hundreds such rotations for residents and fellows across the country and globally.

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!