Dr. Anthony Chang

“If you want to build a ship, don't drum up the men to gather wood, divide the work and give orders. Instead, teach them to yearn for the vast and endless sea.”

Antoine de Saint-Exupery, French pilot and author/poet

There are currently about 682 people who qualify to be astronauts according to the FAI definition of 100km altitude, and majority of these astronauts are engineers in addition to having some piloting skills. I do not believe there are that number of clinicians who have a current era data science and AI education and background (my personal estimate is about 100-200 worldwide). As there are an estimated 100 million clinicians on the global scale, including 10-12 million physicians, it would be strategic to have at least 1% be formally educated and trained in AI to push the AI agenda in medicine and healthcare. This means that we can benefit from about 1 million clinicians to be facile with AI in healthcare (instead of the current 0.0002%).

Below is a suggested and recommended educational strategy for the small coalition of the willing:

 

Masters Degree in AI in Healthcare (or Related Areas). A master’s degree program in artificial intelligence in healthcare provides a structured, in-depth pathway for learners who wish to gain advanced proficiency in this domain, making it a valuable asset in the overall landscape of AI education. Unlike short courses or workshops, a master’s curriculum allows for comprehensive exploration of both technical and clinical dimensions over an extended period, blending coursework in machine learning, natural language processing, data governance, and ethics with modules that emphasize healthcare applications, policy, and leadership. For clinicians, such programs build the capacity to critically appraise AI research and review AI vendors, collaborate effectively with data scientists, and contribute to translational projects that bridge the gap between laboratory innovation and bedside implementation. For non-clinical professionals—such as administrators, engineers, or policy makers—these programs provide the clinical context necessary to design and deploy solutions that are safe, equitable, and sustainable. Importantly, many AI in healthcare master’s programs emphasize capstone projects or practicum experiences within health systems, enabling students to apply knowledge to real-world problems such as predictive modeling in intensive care, imaging diagnostics, or operational efficiency. By combining technical literacy, clinical relevance, ethical awareness, and implementation science, a master’s program not only equips graduates with practical expertise but also helps cultivate the next generation of leaders capable of guiding AI adoption responsibly across healthcare organizations.

There are a few such dedicated health-specific masters programs in AI in healthcare currently, but there are many masters programs in data science in healthcare programs (Stanford, UCLA, UCSF, USC, Harvard, Georgetown, Dartmouth, University of Michigan, etc). In addition, some masters programs in informatics are now incorporating a few data science topics in the curriculum. In the US, there are several masters programs that are dedicated to AI in medicine or healthcare: Masters in AI in Medicine (University of Alabama at Birmingham); Masters of AI in Medicine (University of Louisville); and Masters Nursing Program in AI (Florida State University). Internationally, there are also several dedicated masters programs in AI in medicine or healthcare: Masters in AI in Medicine (University of Bern, Switzerland); Masters in Data Analytics and AI in Health Sciences (DAIHS); and Masters in Precision Health and Medicine (National University of Singapore, or NUS) that combines AI and genomics.

 

AI in Healthcare Fellowship. An AI in healthcare fellowship program (being planned), modeled after the successful structure of clinical informatics fellowships, would be a valuable asset in preparing the next generation of leaders who will guide the responsible integration of AI into health systems. Such a program would provide advanced, post-graduate training that blends hands-on clinical experience with formal didactics in machine learning, natural language processing, ethics, data governance, and implementation science. Fellows would rotate through diverse domains—clinical departments using AI-enabled tools, data science teams developing algorithms, hospital committees reviewing adoption proposals, and regulatory or policy offices evaluating compliance—thereby gaining a 360-degree perspective of how AI influences patient care, operations, and health policy. An AI in healthcare fellowship would cultivate translational leaders who can critically appraise algorithms, anticipate unintended consequences, lead multidisciplinary teams, and communicate effectively across clinical, technical, and administrative boundaries. The capstone projects would give fellows the opportunity to transform theoretical learning into real-world impact. By producing graduates who are equally fluent in the languages of medicine, technology, and governance, such fellowships would ensure a pipeline of future-ready leaders capable of steering healthcare organizations through the opportunities and challenges of AI adoption.

There are very few such dedicated health-specific fellowship programs in AI in healthcare currently. In the UK, there is the Fellowship in Clinical AI (London AI Center). In the US, there are a few noteworthy AI in health fellowships: AI in Human Health Fellowship (Mount Sinai and Icahn School of Medicine); AI Health Data Science Fellowship (Duke); Radiology Informatics and AI Fellowship (Mayo Clinic); AIM-AHEAD Clinicians Leading Ingenuity IN AI Quality (CLINAQ) Fellowship (NIH); and Biodesign AI Fellowship (UCLA). It should be noted that none of these fellowships lead to any board certifications as there is currently no board certification in AI in health (as in clinical informatics under the American Board of Preventive Medicine);.there is, however, some ongoing discussion about the possibility of a board certification in AI in Health in the near future.

 

Doctorate Degree in AI in Healthcare (or Related Areas)(DSc or PhD). A doctorate degree program in artificial intelligence in healthcare represents the highest level of structured education in this domain and is uniquely positioned to cultivate leaders who can shape the future of AI in medicine and healthcare. Unlike master’s programs, which emphasize applied knowledge and professional skill-building, a doctorate allows for deep scholarly inquiry, original research, and leadership development that extend beyond technical proficiency. Such programs train the doctoral candidates to critically evaluate and generate new knowledge in areas such as machine learning for clinical prediction, natural language processing of health records, algorithmic fairness, and the governance and economics of AI adoption. Importantly, they also incorporate domains often overlooked in purely technical training—such as implementation science, health systems leadership, ethical principles, regulatory science, and transdisciplinary collaboration—ensuring graduates can bridge the gap between technological innovation and clinical reality. For clinicians pursuing doctoral-level study, these programs offer the chance to contribute novel insights directly relevant to patient care and health equity, while for data scientists, they provide immersion in the complexities of healthcare delivery. The capstone dissertations or translational research projects typically involve partnerships with hospitals, government agencies, or industry, giving doctoral candidates a platform to influence real-world policy and practice. By producing clinician–scientists, academic leaders, and policy innovators with advanced expertise, doctoral programs in AI for healthcare ensure that the next generation of professionals are not only literate in AI but are also architects of responsible, sustainable, and human-centered integration of AI into global health systems.

There are very few such dedicated health-specific doctorate programs in AI in healthcare currently: PhD in AI for Healthcare (Imperial College London); PhD in AI for Biomedical Innovation (University of Edinburgh); PhD in Health AI (Cedars Sinai); PhD concentration in AI and Emerging Technologies in Medicine (Mount Sinai and Icahn School of Medicine); PhD in Biomedical Informatics with AI in Medicine track (Harvard Medical School); and the upcoming PhD/DSc program in AI in Health and Medicine for Healthcare Leaders (University of Alabama School of Medicine) that I will be leading with Dr. Rubin Pillay.

 

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!