What do sick care professionals need to know about artificial intelligence?

With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students, trainees and active clinicians be taught. Or, for that matter, whether medical students, for example, should be taught AI at all.

Many are advocating for AI literacy and dexterity in K-12.

Only 15% of managers consistently use gen AI, but 40% of business graduate students do. These managers and leaders of the near future will soon enter a workforce that is underprepared for them and poorly designed for them to put their abilities to use. This article explores how organizations should respond. Companies should learn to become a magnet for gen AI-savvy talent, and discover how to best onboard, engage, integrate, and retain the next generation of AI-capable managers

Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

“Digital academies” are among the most successful approaches to closing the digital skills gap. These initiatives are specific to the company’s culture and narrative, are highly experiential and considerate of organizational team dynamics, and reach across the enterprise. Using DuPont’s digital academy as a model, companies should design their own internal upskilling programs to serve broad employee segments, include experiential elements, encourage continuous engagement, and prioritize flexibility.

Young professionals are serious about learning digital skills this year, according to LinkedIn’s Workforce Confidence survey, with 43% of Gen Z-ers wanting to learn data analysis and 31% wanting to learn AI-related skills, such as prompt engineering. Millennials want to boost their expertise in data analytics (39%) and AI skills (32%), too. Learning interest dwindles for Gen X when it comes to both areas (29% and 26%, respectively). And on the other end of the generational spectrum, half of baby boomers express no desire to learn any digital skills.

While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security.

Premed, medical school, postgraduate and CME curriculum design begins with a needs assessment and gap analysis that drives learning objectives that should be addressed by the curriculum. Here is the case for data literacy and digital health education and training. The new triple threat is clinical competence, data literacy and dexterity and business skills.

Some have suggested that the learning objectives should be:

1. Use it—identify when the technology is appropriate for a given clinical context, and what inputs are required to receive meaningful results. It should supplement how we think, not what we think.

2. Interpret it—understand and interpret the results with a reasonable degree of accuracy, including awareness of sources of error, bias, or clinical inapplicability. Models developed for COVID-19 patients could be an example of AUC in practice, providing risk estimates for the outcome of patients with COVID-19 (e.g., 4C Mortality Score).

3. Explain it—be able to communicate the results and the processes underlying them in a way that others (e.g. allied health professionals and patients) can understand.

I would add:

4. Create it- be able to design, develop and disseminate AI products and services either internally, as an intrapreneur, or externally as AI entrepreneur.

5. Lead it- be able to lead AI innovators as an AI leaderpreneur. Here is how healthcare executives can get started with artificial intelligence.

6. Advocate for it-be able to inform and direct policy initiatives concerning the responsible , equitable, secure and ethical use of AI.

7.Work in it i.e. with or for the AI industry as a side gig or non-clinical career.

8. Teach with it

9. Think with it

10. Increase productivity with it

11. Use it to make clinical decisions

In this paper, the authors have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI entrepreneurship which sits at the intersection of digital health, artificial intelligence and entrepreneurship.

This study developed the MAIRS-MS and evaluated its reliability and validity, which aimed to measure medical AI readiness of medical students. The overall results showed good reliability and validity of the MAIRS-MS in medical students. The scale consisted of 22 items, and EFA revealed that the MAIRS-MS had four factors: cognition, ability, vision, and ethics (Additional?file?3). To investigate the concurrent criterion validity, the relationship of MAIRS-MS with a criterion (gold standard) measurement could not be applied as it is the first defined scale that is developed related to the subject.

The cognition factor of the readiness scale includes the items that measure the participant’s cognitive readiness in terms of terminological knowledge about medical artificial intelligence applications, the logic of artificial intelligence applications and data science. The ability factor of the scale includes items that measure the participant’s competencies in terms of choosing the appropriate medical artificial intelligence application, using it appropriately by combining it with the professional knowledge, and explaining it to the patient. The vision factor of the scale includes items that measure the participant’s ability to explain limitations, strengths and weaknesses related to medical artificial intelligence, anticipate opportunities and threats that may occur, and conduct ideas. Scale items under the ethics factor measure the participant’s adherence to legal and ethical norms and regulations, while using AI technologies in healthcare services.

The ultimate objective and key results of medical education AI initiatives should be to create graduates who can win the 4th industrial revolution by using AI and other analytics tools to help achieve the quintuple aims.

In addition, there are several ways to us AI to measure knowledge, skills, abilities and compentencies after employee training and development interventions.

All health and care staff – especially GPs – should be trained in artificial intelligence (AI), a new report by Health Education England (HEE) and the NHS AI Lab has found.

HEE announced the publication of its?joint report?setting out recommendations for health education and training providers in England so that they can ‘plan, resource, develop and deliver new training packages on AI for health and care staff’.

These authors propose 6 domains of competency for the effective deployment of AI-based tools in primary care: (1) foundational knowledge (what is this tool?), (2) critical appraisal (should I use this tool?), (3) medical decision making (when should I use this tool?), (4) technical use (how do I use this tool?), (5) patient communication (how should I communicate with patients regarding the use of this tool?), and (6) awareness of unintended consequences (what are the “side effects” of this tool?).

This paper defined the competencies as:

The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care.

These 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.

Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care.

Here are other thoughts and suggestions.

The Joe R. and Teresa Lozano Long School of Medicine at The University of Texas Health Science Center at San Antonio (UT Health San Antonio) and the University College at The University of Texas at San Antonio (UTSA) are working to prepare the next generation of clinicians for further integration of advanced technologies through the United States’ first dual degree program combining medicine and AI.

In this scoping review, of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.

Many academic, industry and public entities are experimenting with different AI education models. We will have to await the outcomes, test the ideas, measure their results and not be blinded by the glare of shiny new objects and the unintended consequences of technofatigue.

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