Vanderbilt's Healthcare AI Sessions spotlight ethics, data security, and startup innovation in health care

Healthcare AI Sessions: Practical Insights from Clinicians, Operators, and Founders

(photo by Donn Jones)

On Sept. 28, the third annual Healthcare Artificial Intelligence Sessions filled Langford Auditorium with clinicians, data leaders, and entrepreneurs. Sponsored by the Brock Family Center for Applied Innovation, the event gathered voices from Vanderbilt University Medical Center and partner organizations to focus on AI's real impact in care delivery, operations, and new ventures.

The sessions ran alongside the Nashville Health Care Council's 2025 Healthcare Sessions (Sept. 29-30), drawing local leaders, students, and national attendees. The conversations stayed grounded: data safeguards, clinical risk, and what it takes to turn AI pilots into dependable practice.

Key takeaways you can use now

• Protect patient data from the start: Limit PHI exposure, apply de-identification where possible, and enforce strict access controls. Align your controls to recognized frameworks such as the NIST AI Risk Management Framework. Build audit trails for model inputs, prompts, and outputs.
• Address safety and ethics for chatbots: Harmful or misleading responses are a clinical and reputational risk. Use human-in-the-loop review for care-facing use, add clear disclaimers, constrain models to verified content, and implement red-teaming and escalation paths.
• Start with narrow, high-yield use cases: Prior authorization support, clinical documentation, imaging worklists, and patient messaging triage are strong candidates. Define baseline metrics, set acceptance thresholds, and timebox pilots.
• Validate across subpopulations: Evaluate bias, calibration, and clinical relevance for age, sex, race, language, and comorbidity groups. Use external validation and drift monitoring before moving beyond pilot.
• Integrate where clinicians live: Reduce click burden. Connect to the EHR, order sets, and existing analytics. Clear ROI stories-minutes saved per note, denials reduced, throughput gained-speed procurement.
• Know the regulatory boundary: If a tool informs diagnosis or treatment, review FDA considerations for software as a medical device. Track updates like the FDA's AI/ML guidance for medical devices.

What stood out in the discussions

• Governance is non-negotiable: Clear policies for data use, model selection, vendor vetting, and incident response reduce downstream risk.
• Ground models in your knowledge base: Retrieval-augmented generation with vetted clinical content lowers hallucination risk and improves consistency.
• Workforce enablement beats workforce replacement: The most success came from tools that save time and improve quality without changing clinical accountability.
• Entrepreneurial realism: Founders emphasized integration effort, security reviews, and multi-stakeholder buying cycles as the real hurdle-not model choice.

Action plan for your organization

• Create an AI governance council with clinical, compliance, security, and patient safety representation.
• Inventory data sources; segment PHI; define approved use cases and redlines.
• Pick 2-3 pilots with measurable value; predefine metrics, timeline, and exit criteria.
• Stand up prompt logging, content filters, and monitoring for drift and adverse events.
• Publish patient- and staff-facing communication on how AI is used and supervised.
• Train clinicians on safe use, limitations, and escalation protocols.

Keep learning

• Upskill your team with role-based programs: AI courses by job