Part 2: Artificial Intelligence (AI) in Healthcare Law & Compliance: A Discussion with Nelson Mullins Partners Bob Coffield & Darren Skyles

Episode Date: January 14, 2026

In this episode of Stark Integrity, Bob Wade (Captain Integrity) continues the discussion on artificial intelligence with Bob Coffield and Darren Skyles, Partners at Nelson Mullins:

How AI is being applied in healthcare law and compliance—and where the real risks are emerging.

This episode is Part 2 of a three-part series, moving beyond foundational concepts to focus on practical application, risk identification, and critical questions organizations must address.

The central message:

AI is already being used in compliance—but organizations must be intentional, thoughtful, and disciplined in how they adopt it.

AI in Action: Compliance Applications

A key theme in Part 2 is:

How AI is currently being used in healthcare compliance.

Examples include:

• Supporting internal audits and monitoring
• Identifying billing anomalies and patterns
• Assisting with documentation review
• Enhancing risk detection and analytics

The takeaway:

AI has the potential to strengthen compliance programs—but only if used appropriately.

The Right Questions to Ask

The discussion emphasizes that:

Successful AI implementation starts with asking the right questions.

Organizations should consider:

• What is the AI tool actually doing?
• How reliable are the outputs?
• What data is being used?
• Who is accountable for the results?

The key point:

Blind adoption creates risk—intentional evaluation reduces it.

Identifying Areas of Greater Risk

Part 2 focuses heavily on:

Where AI creates the greatest exposure.

Higher-risk areas include:

• Clinical decision support
• Billing and revenue cycle activities
• Patient-facing tools
• Documentation generation

Because:

Errors in these areas can directly impact compliance, reimbursement, and patient care.

The takeaway:

The higher the stakes, the greater the need for oversight.

Bias and Fairness in AI

A major topic in this episode is:

Bias in AI systems.

AI tools may:

• Reflect biased training data
• Produce inconsistent or unfair outcomes
• Create disparities in treatment or decision-making

The key point:

Being unbiased and fair is critical in healthcare.

Organizations must evaluate:

• How AI models are trained
• Whether outputs are consistent
• How bias is identified and mitigated

The takeaway:

Bias is not just a technical issue—it is a compliance and legal risk.

Could Not Using AI Become a Risk?

One of the more forward-looking questions raised in the episode is:

Will failure to use AI eventually be considered negligence or malpractice?

As AI becomes more widespread:

• It may enhance accuracy and efficiency
• It may become an industry standard
• Expectations for its use may increase

The key point:

The risk may shift from using AI—to not using it.

Balancing Innovation and Risk

The episode reinforces the need to:

Balance the benefits of AI with its risks.

Organizations must:

• Encourage thoughtful innovation
• Avoid reckless implementation
• Maintain compliance discipline

The takeaway:

AI should be adopted strategically—not impulsively.

Governance and Accountability

Part 2 underscores:

The importance of governance in AI use.

Organizations should establish:

• Clear accountability for AI decisions
• Oversight mechanisms for outputs
• Defined roles and responsibilities

Because:

Without governance, AI creates uncontrolled risk.

Practical Considerations

To manage AI effectively, organizations should:

• Evaluate AI tools before deployment
• Test outputs for accuracy and reliability
• Monitor performance over time
• Train staff on proper use and limitations

The key point:

AI is not “set it and forget it”—it requires ongoing oversight.

Common Pitfalls

The episode highlights several risks:

Overreliance on AI

Assuming outputs are always correct without validation.

Lack of Transparency

Not understanding how decisions are made.

Ignoring Bias

Failing to assess fairness and consistency.

Weak Governance

Not establishing clear ownership and oversight.

The takeaway:

AI amplifies both strengths and weaknesses in compliance programs.

Key Takeaways

• AI is already being used in healthcare compliance
• Asking the right questions is critical before implementation
• Certain use cases carry higher risk
• Bias and fairness are major compliance concerns
• The legal standard of care may evolve with AI adoption
• Governance and accountability are essential
• Ongoing monitoring is required for defensibility

Final Thoughts

Part 2 builds on the foundation established in Part 1 and introduces a critical shift:

From understanding AI to actively managing it.

As AI becomes more embedded in healthcare:

• Expectations will increase
• Risks will evolve
• Oversight will become more important

Ultimately:

The question is no longer whether to use AI—but how to use it responsibly.

Because in healthcare compliance:

AI is not just a tool—it is a risk multiplier that must be carefully governed.

Click here to listen to this Stark Integrity Podcast Episode:
https://podcasts.apple.com/us/podcast/part-2-artificial-intelligence-ai-in-healthcare-law/id1588939373?i=1000745987779&l=fr-FR