Grand Rounds: Research in the Age of Artificial Intelligence: Promises & Pitfalls

Objectives (Educational Content) :

1. Identify and describe at least three major risks associated with AI use in biomedical research, including automation bias and semantic drift. Action: Learners will be able to recognize and articulate specific pitfalls and perils of AI in research settings.

2. Apply ethical and regulatory standards (e.g., HIPAA, authorship guidelines) to ensure compliance when integrating AI tools into research workflows. Action: Learners will demonstrate how to maintain data confidentiality and proper attribution when using AI in research

3. Develop a practical plan for incorporating AI as an asset in their own research projects, including transparent disclosure and human oversight. Action: Learners will outline steps to responsibly leverage AI for impactful discovery, while maintaining accountability and integrity

Target Audience:

General pediatricians, family physicians, nurse practitioners, physician assistants, social workers, psychologists, and nurses.

Identified Gap:

Biomedical researchers are increasingly using artificial intelligence tools in research workflows, yet many lack the necessary knowledge and competence to recognize AI-specific risks (such as automation bias, hallucinations, and semantic drift), apply ethical and regulatory standards appropriately, and implement transparent human oversight. This gap results in inconsistent, informal, or noncompliant AI use that may compromise data integrity, confidentiality, authorship attribution, and trust in scientific findings. There is a clear need for education that equips researchers to critically evaluate AI outputs, ensure compliance with ethical and regulatory requirements, and intentionally integrate AI as a supportive, accountable tool in biomedical research.

Estimated Time to Complete the Educational Activity:

1 hour(s)

Expiration Date for CE/CME Credit:

04-09-2027

Method of Participation in the Learning Process:

The learner will view the presentation, successfully complete a post-test and complete an activity evaluation.

Evaluation Methods:

All learners must successfully complete a post-test, as well as an activity evaluation, to claim CE/CME credit.

Disclosure:

The speaker has returned the disclosure form, indicating that the following financial relationships with ineligible companies: Novartis (Relationship ended) - Michael Forbes, MD The following CME Committee/Planning Committee member(s) has the following financial relationships with ineligible companies to disclose: Merck - Maria Cristina Victorio, MD All financial relationships have been mitigated.

Accreditation Statement:

Children’s Hospital Medical Center of Akron is accredited by the Ohio State Medical Association to provide continuing medical education for physicians.

CHMCA designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit TM.  Physicians should only claim the credit commensurate with the extent of their participation in the activity.

Bibliography:

1. Bienefeld N, Keller E, Grote G Human-AI Teaming in Critical Care: A Comparative Analysis of Data Scientists’ and Clinicians’ Perspectives on AI Augmentation and Automation J Med Internet Res 2024;26:e50130

2. https://theconversation.com/where-does-human-thinking-end-and-ai-begin-an-ai-authorship-protocol-aims-to-show-the-difference-266132 Accessed December 11, 2025

3. https://nodesian.medium.com/technically-accurate-medically-fatal-a3bbbc7c27cc Accessed December 19, 2025 McCradden MD, Thai K, Assadi A, et al. BMJ Evidence- Based Medicine 2025;30:183–193.