Overview of Healthcare AI Research

Artificial intelligence is reshaping healthcare. From clinical decision support to generative AI scribes, new tools are entering patient care at record speed. But with innovation comes risk — risks to trust, safety, and equity.

At AI Healthcare Compliance, our mission is twofold:

1. Push the future forward by advancing methods that make healthcare AI safer, fairer, and more transparent.

2. Support the ecosystem with content that translates complex regulations, risks, and best practices into clear, practical guidance.

We also invite researchers, clinicians, and builders to participate and contribute — because no single team can address these challenges alone. Together, we can create AI systems worthy of trust in healthcare.

The Focus of Our Research

Our current work examines four urgent challenges shaping the future of healthcare AI:

1. How do new and existing regulations apply to healthcare AI?

AI is moving faster than regulation, but lawmakers are catching up. Global frameworks like the EU AI Act (2024), U.S. FDA’s AI/ML regulatory guidance, and Canada’s Artificial Intelligence and Data Act (AIDA) are creating new compliance demands for healthcare innovators.

Our research investigates how these emerging rules interact with existing healthcare privacy laws such as:

We are mapping how these overlapping frameworks apply to clinical AI tools and what compliance challenges clinics, startups, and developers must prepare for.

 

2. How can we detect and prevent hallucinations in generative AI?

Generative AI can create convincing but false information — known as “hallucinations.” In healthcare, this risk is profound: an inaccurate summary in a patient record or a fabricated reference in a clinical note could compromise care.

Our research focuses on:

  • Detection methods: techniques like uncertainty quantification, retrieval-augmented generation (RAG), and model alignment.

  • Safeguards: designing AI systems that flag low-confidence outputs before reaching clinicians.

  • Evaluation frameworks: working with healthcare stakeholders to define acceptable error thresholds.

Resources:

 

3. How do population and language biases affect healthcare AI performance?

Bias is not theoretical — it shows up in real-world AI systems. If a model is trained on English-only datasets, or lacks representation from specific populations, its performance drops for those groups. This can worsen health disparities and erode trust.

Our research aims to:

  • Identify gaps in training datasets for underrepresented populations.

  • Develop bias testing protocols for healthcare AI before deployment.

  • Explore language model adaptation for non-English contexts and diverse medical terminologies.

Resources:

 

4. How can we improve model explainability for clinical AI?

For healthcare, a “black box” is not enough. Clinicians, regulators, and patients need to understand why an AI made a recommendation. Without explainability, trust — and accountability — break down.

We study approaches to:

  • Decision tree and rule-based transparency for high-stakes applications.

  • Visual explanation tools that help clinicians interpret AI outputs.

  • Regulatory expectations for explainability under frameworks like the EU AI Act, which requires certain AI systems to be auditable and interpretable.

Resources:

 

How This Helps

Healthcare AI will only succeed if it is trusted. That trust comes from:

  • Regulatory compliance – meeting HIPAA, GDPR, PHIPA, and the new wave of AI-specific laws.

  • Technical safeguards – preventing hallucinations, bias, and opaque “black box” decisions.

  • Knowledge-sharing – helping innovators and clinicians understand risks and adopt best practices.

That’s why our work doesn’t stop at research. Each insight is added to our content library, where practitioners can find explainers, guides, and frameworks that make navigating this complex space simpler.

The Bottom Line

AI in healthcare is not just about technology — it’s about responsibility, safety, and trust.

By conducting research and keeping a public library of content, we aim to:

  • Push the future forward with safer, fairer, more transparent AI.

  • Support the ecosystem with guidance that helps clinics, startups, and regulators.

  • Invite others to contribute and collaborate, so healthcare AI evolves with accountability at its core.