Evaluating AI Platforms: Key Insights for Health Care Providers

Understanding the Role of AI in Health Care

Large language models are increasingly being used for tasks such as generating patient communication drafts, summarizing medical literature, and supporting clinical decision-making. However, the adoption of these tools requires a careful balance. While the potential benefits are significant, there are risks of inaccuracies, unintended outputs, and legal implications that must be addressed to ensure patient safety and compliance.

For providers, the challenge lies in evaluating which AI models are appropriate for their needs and how best to integrate these tools into their workflows.

Red-Teaming: A Proven Strategy for Safer AI Deployment

One of the key strategies discussed during the webinar was “red-teaming,” a concept borrowed from cybersecurity. This involves testing AI systems by simulating potential misuse or unintended outcomes to identify and mitigate risks.

Red-teaming can help uncover vulnerabilities, such as:

• Legal Risks: Exploring scenarios where a model could inadvertently generate outputs that violate laws or regulations.
• Sociotechnical Harms: Identifying how the model might perpetuate biases or produce misleading information.

While red-teaming is crucial throughout the AI lifecycle—design, development, validation, deployment—there are no universal standards for testing. The webinar speakers emphasized the importance of combining internal testing with periodic evaluations by third-party experts to strengthen accountability and reliability.

Real-World Applications and Challenges

Case studies from the webinar highlighted both the promise and limitations of AI in health care. For example, when an LLM was used to draft responses to patient inquiries, around 6% of the drafts contained inaccuracies, often referred to as “hallucinations.” This underscores a key point: all AI-generated outputs are approximations, and they must be validated by human experts to ensure accuracy and relevance.

Providers must also be mindful of how LLMs interact with the conventions of medical practice. For instance, these models may not prioritize the use of recent literature, which is critical in evidence-based medicine.