Implementing AI Into Healthcare Education: The Ethical and Legal Landscape

Artificial intelligence (AI) is revolutionizing healthcare education, offering opportunities to improve efficiency and outcomes; however, it comes with ethical and legal challenges that healthcare professionals must navigate.

At this year’s Alliance Annual Conference, Nuria Negrao, PhD; Alexandra Howson, PhD, CHCP; Morgan Leafe, MD, MHA; and Sara Fagerlie, PhD, CHCP, presented a session on navigating the ethical and legal landscape of AI in healthcare education. In this interview, the four further delve into the challenges faced with implementing AI into healthcare education, strategies to combat these challenges, and the importance of demystifying AI in order to responsibly integrate and use it to its fullest potential in continuing education (CE) programs and activities.

Ethical and Legal Challenges of AI in Healthcare Education

Bias and Plagiarism
Generative AI tools can amplify bias present in their training data, leading to skewed outputs. “We know that the content retrieved from a lot of generative AI tools is already biased and amplifies that bias in some curious ways,” noted Dr. Howson, a medical writer and educator. Plagiarism is another concern, as these tools often produce content without clear attribution or verified sources. This lack of transparency demands rigorous fact-checking by users to ensure content credibility.

Copyright and Consent Issues
AI systems often use copyrighted materials without proper permissions, raising legal questions. “As it stands, the US Copyright Office does not recognize content developed by non-humans,” explained Dr. Negrao, an AI adoption strategist. This means AI-generated materials lack copyright protection. Additionally, using learner data in AI systems, even in anonymized forms, raises ethical concerns about consent.

General Lack of Transparency
AI systems are predominantly developed by private entities, creating a lack of clarity about how these tools function. “Some of the burden of learning, of course, is on us to use the resources available to learn how these tools work, but there is a general lack of transparency,” Dr. Howson emphasized.

Strategies for Addressing Challenges

Developing Multidisciplinary Guidelines
Creating internal AI policies tailored to organizational needs is critical. These policies should be more nuanced than outright bans and focus on proper usage, training, and oversight. “Having people within the committee who know how AI can be used for specific roles or have use cases of how it will be used is really important,” said Dr. Fagerlie, an oncology content development specialist and educational strategist.

Fostering Education and Transparency
Education on AI tools and their limitations is key. Professionals should learn how to craft effective prompts and use plagiarism detection tools to enhance AI’s utility while maintaining ethical standards. Transparency about AI’s role in content creation also builds trust and accountability. “Approach AI with curiosity instead of assumptions,” advised Dr. Leafe, a physician and medical writer. “It’s a tool to help humans do their job better.”

Starting Small and Benchmarking
Experts recommend starting with manageable AI tasks, such as automating repetitive processes or enhancing content creation. Benchmarking outcomes for quality and efficiency can help refine AI integration. Dr. Negrao suggested, “Look at tasks you do all the time that are very repetitive and try to figure out if there is an AI tool that can effectively help with that task.”

Embracing AI with Curiosity

Experts encourage approaching AI with curiosity and adaptability. Viewing AI as a tool to enhance, not replace, human effort ensures that healthcare education benefits from its capabilities. “Right now, it offers a lot of opportunities if used properly to enhance healthcare education,” noted Dr. Leafe. “We really need to be taking advantage of those opportunities because if we don’t, we’re not helping our learners.”

Key Takeaways for Healthcare Professionals

1. Understand AI’s Risks and Benefits: AI has immense potential but requires human oversight to mitigate bias, plagiarism, and ethical concerns
2. Develop Robust Policies: Establish clear, actionable guidelines for AI use within organizations
3. Leverage Multidisciplinary Expertise: Include diverse stakeholders in AI decision-making processes
4. Start Small: Begin with specific, low-risk applications and expand as confidence and expertise grow
5. Stay Informed and Curious: “Don’t bury your head in the sand,” Dr. Negrao advised. “Find one source of information you trust and keep tabs on what is happening”

By addressing these challenges and adopting thoughtful strategies, healthcare professionals can harness AI’s potential while safeguarding ethical and legal standards.

About the Speakers

Nuria Negrao, PhD, is a medical writer specializing in CME/CE content creation, as well as an AI adoption strategist. Dr. Negrao is passionate about integrating AI and automation into the field of healthcare education, encouraging and empowering other medical writers to be curious and expand their knowledge on the subject.

Alexandra Howson, PhD, CHCP, is a medical writer, a qualitative researcher, an educator, and hosts Write Medicine, a weekly podcast exploring best practices in creating continuing education content for health professionals. Her most recent book is WriteCME Roadmap: How to Thrive in CME with No Experience, No Network, and No Clue (Tilt Publishing, 2024).

Morgan Leafe, MD, MHA, is a medical writer, physician, and consultant. Dr. Leafe is passionate about developing patient-facing, clinician-facing, and healthcare education content. In addition, she assists individuals in creating custom resumes and LinkedIn profiles.

Sara Fagerlie, PhD, CHCP, has extensive experience in academic cancer research, medical news, and developing curricula and education materials for healthcare professionals. She is the Founder of Cancer Communicator, LLC, with a mission to improve the care of patients with cancer through clear and accurate communication of clinical data and evidence-based practice.