Strategies for leading medical professional AI education, training and development: closing the gaps

As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.

According to this report, a number of potential barriers to implementing these technologies exist; the 3 main limitations identified include regulatory, economic, and organizational culture issues [15]. Regulatory approval [16] is needed to adopt AI technologies in clinical settings, and potential liabilities in using these technologies for patient care must be considered, as well as the safety, efficacy, and transparency of AI algorithms for clinical decision-making [17,18]. Regulatory issues can also come into play when it comes to accessing data for AI adoption; multi-institution data sharing is required for algorithm improvement and validation, as well as the accompanying research ethics board and regulatory approvals [18]. To further improve adoption, these technologies will also have to be economical, supported by adequate funding [18], and seem as valuable to the organization itself. At an organizational level, the use of AI should align with the goals and strategic plans of an organization; organizations will need to assess how well the AI technology will integrate into existing systems, including data warehouses and electronic health records [18]. It may be difficult to generalize a particular AI model across different clinical contexts to a degree that would prove valuable at an organizational level while still working seamlessly and being clinically useful at the individual level [15]. Furthermore, when choosing to adopt AI technologies, organizations can either collaborate with outside vendors or create the technologies in-house, which will require the use of additional human and material resources [15].

Here are some guiding principles that could be used to guide the development of future AI curricula or to incorporate AI education into existing curricula:

Principle 1: Need for Regulatory Strategies Accreditation standards requiring teaching to the test and forgetting the rest needs to change.

Principle 2: Multidisciplinary Approach to Design and Delivery: We need to remove innovation siloes and support interprofessional entrepreneurship

Principle 3: Competence-Based Curriculum Design: Using AI to teach AI using problem and project-based learning competencies

Principle 4: Patient-Clinician Interaction: How to be an intrapreneurial clinical champion

Principle 4: Team teaching faculty development:

Principle 5: Organizational Innovation and AI readiness

Principle 6: Innovation ecosystem integration

Principle 7: The people part of AI

Principle 8: Are you ready to innovate-mindset, means, motivation?

Principle 9: Principles of Medical Education and Training Reform

Principle 10: Think big. Start small. Stay small for the right amount of time

In addition, there are differences between education, training, and development that require different dissemination and implementation strategies.

Development is an abstract art—it is the nature of watching a skill or person grow over time. It is a continual improvement toward an overarching goal that can be more informal and is observed relationally over time (Example: a mentor observing a mentee).

In education, there are facts in figures. There is a conceptual understanding that everyone should be able to reach when successfully trained.

On the other hand, training is a vessel by which education is achieved. Training is meant to teach knowledge, skill, or attitude (KSA) that can be immediately understood and implemented.

Leading AI education, training and development requires knowledge, skills, attitudes and competencies in three overlapping domains.

The first is edupreneurship. Entrepreneurship means many things to many people. The biggest misconception is that entrepreneurship refers, exclusively, to starting businesses. In fact, if we use the definition that entrepreneurship is the pursuit of opportunity with scarce or uncontrolled resources with the goal of creating user defined value through the deployment of innovation using a VAST business model then it means much more. In fact, there are many ways to innovate and create user defined value, whether it be in sick care or education, other than taking care of patients, starting a business or teaching students. Edupreneurs create education, training and development value, typically within their organization. It is a specific kind of intrapreneurship.

Another application is AIntrepreneurship, creating AI products and services. Artificial intelligence has many potential and demonstrated applications in clinical care. This course is for healthcare professional students who are interested in attending an introductory course about the fundamentals of artificial intelligence technologies. their clinical applications and the ethical, legal, societal and deployment issues they present.

Pre-seed venture capital company Techstars, CareFirst BlueCross BlueShield, and Johns Hopkins University, have entered a partnership to initiate a healthcare accelerator program in Baltimore, US. This initiative aims to nurture early-stage entrepreneurs focused on enhancing healthcare through artificial intelligence (AI).

The second is transdisciplinary informed subject matter expertise. The digital transformation strategists are telling us that it requires cultural change, process improvement, technology, and workforce upskilling. But, more importantly, digital transformation, particularly in applications of artificial intelligence in sick care, requires close collaboration between multiple clinical and technology stakeholders including not just computer scientist but bioengineers too.

The third is leading organizational change. Sick care is undergoing massive change, i.e. dislocation more than disruption, but the players, particularly doctors, seem to be responding to the political, technological, and economic pressures, rather than leading the effort, creating a coherent vision, and giving direction and inspiration.

Leading AI education change requires a cadre of new triple threats: clinician-technologist-leaderpreneur.

Given the sclerotic state of medical education and training reform and the lack of AI and entrepreneurship education in medical schools and residency training, it will likely take a generation to create them, but now is the time to take first steps, since technology innovation knowledge gaps are growing exponentially and far outpacing our ability to adapt to them.

Why does it take a medical technology 17 years to become famous overnight?

Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs on Substack