Written by: WSCL Marketing Team
Walk into any hospital, and you can see it everywhere: AI is already integrated into its operations. It flags suspicious spots on scans, nudges care teams when a patient’s risk spikes, and trims the paperwork that no one misses doing. In other words, it makes life easier. But here’s the problem: the healthcare rulebooks weren’t written for self-learning software. Lawmakers and regulators built frameworks like HIPAA, HITECH, the FDA, and CMS long before AI models could update themselves or process vast stores of patient data. So the question isn’t whether AI will reshape care; it already has. The question is whether our regulations can keep up. And just as important: do we have people trained to keep on top of it? That’s exactly where healthcare, law, and technology meet—and where Western State’s MLS in Healthcare Compliance steps in.
Understanding the Regulatory Landscape
Before we get into how AI is rewriting the rulebook, it’s useful to take a look at the rules as they currently stand. In healthcare, compliance has long been built around laws like HIPAA and HITECH—our go-to safeguards for protecting patient privacy and securing medical data. The FDA oversees the safety of medical devices, which now includes “software as a medical device,” or SaMD, and the Centers for Medicare & Medicaid Services (CMS) handles reimbursement and quality standards.
In Europe, the General Data Protection Regulation (GDPR) sets a global tone for how sensitive information, especially health data, should be handled. Together, these frameworks form a web of protection that has worked well for decades. But they weren’t built for a world of self-learning algorithms.
AI is constantly adapting and evolving. Machine-learning systems learn, adapt, and even update themselves, which makes today’s regulatory frameworks feel somewhat outdated. Regulators now understand this and acknowledge that traditional rules weren’t built for technologies that think, change, and improve in real time. Obviously, changes need to be made.
Where AI Meets Regulation: Key Pressure Points
What exactly are the points that pose problems when AI meets regulation in healthcare? Let’s explore four major ones.
Data Privacy and Security
AI thrives on data—lots of it. Patient records, imaging files, genomics, sensor data from wearables: these feed the algorithms. That raises significant questions. How do we anonymize or de-identify data when the algorithm might reverse-engineer patterns? What steps can we take to guard against leaks of sensitive information from models trained on patient data? And are the datasets used in AI truly compliant with HIPAA, HITECH, and other regulations? And what about cybersecurity: AI-enabled devices (or platforms) may become new targets, or may behave unpredictably in newer contexts. The regulatory bodies are increasingly conscious of these risks.
Bias and Fairness
Algorithms are only as good as the data we feed them. If the data reflects bias, for example, not enough data on certain demographics, the AI will learn that bias. This can lead to misdiagnoses, unequal treatment, or worse outcomes for already marginalized patients. Regulators are starting to require that algorithms be tested for fairness and transparency in addition to accuracy, to ensure the technology serves everyone equally.
Accountability and Liability
Here’s an example of a tricky situation: if an AI system makes an error, who’s responsible for it? The person who built it? The hospital that deployed it? Or the physician who used it? The law doesn’t have a clear answer for this yet. “Algorithmic accountability” is becoming a hot topic in policy circles as courts and lawmakers grapple with how to assign fault in a system that’s partly automated and partly human.
Transparency and Explainability
AI often works like a black box: it spits out answers without showing how it came to that conclusion. In medicine, that’s just not good enough. Patients and clinicians need to understand why a decision was made as it was. Regulators are increasingly pushing for explainable AI, where developers document how models were trained, what data was used, and what their limitations are. The goal is to build trust in systems like AI that make life-changing decisions.
How Regulators Are Responding
The good news is that these issues are actively being worked on by regulators, rethinking how to oversee tech that evolves in real time.
The FDA, for example, now treats “Software as a Medical Device” (SaMD) as its own category—software that performs a medical function without hardware attached. In 2025, the agency issued draft guidance for AI-enabled devices focused on documentation, transparency, bias prevention, and post-market monitoring. Instead of a one-time approval, the FDA’s “total product lifecycle” approach recognizes that algorithms will change and need continuous oversight. They know the current rulebook doesn’t fit, so they’re writing a new one. The agency is encouraging earlier collaboration with developers, clearer record-keeping, and safeguards that evolve in tandem with the software.
State and international regulators are doing the same. For example, California’s Privacy Rights Act (CPRA) strengthens data protections, and the European Union’s AI Act has strict requirements for these “high-risk” systems. Together, these efforts point toward adaptive regulation—one designed to keep innovation moving while protecting the people AI is meant to serve.
The Ethical and Legal Balancing Act
Let’s pause for a moment and reflect: AI in healthcare isn’t just a technical or regulatory issue. It’s an ethical and legal one too. Every time a hospital introduces a new AI tool, it raises a familiar question: who’s actually making the call—the doctor, or the AI?
These questions compound when healthcare becomes dependent on technology. How do we get informed consent when an algorithm helps guide a diagnosis or treatment plan? Who owns the training data these systems use? How can the data be shared responsibly? What happens when bias affects the results and produces unequal outcomes for patients? And if something goes wrong, who should be held liable?
With the advent of AI, these scenarios are actually happening in hospitals right now. For healthcare organizations and legal teams, that means rebuilding governance by creating oversight committees, documenting testing and updates, and training staff to responsibly interpret the answers they get from these algorithms.
For professionals stepping into this space, like students in Western State’s MLS in Healthcare Compliance, this is where challenge becomes opportunity. The future of healthcare will depend on people who know how to bridge ethics, data, and law, and make sure innovation comes with accountability.
Emerging Areas of Regulation
There are several key areas around regulating AI that will become a point of focus as compliance works on catching up with AI.
One is AI Auditing and Certification. Policymakers are already pushing for regular check-ins to verify that algorithms remain safe, fair, and accurate while they’re being used. Another is Data Provenance Rules: regulators want to be able to trace the origin of training data to know how it was labeled, validated, and updated, and ensure it represents the full range of patients it’s meant to serve. Then there’s Dynamic/Adaptive Regulation: because many AI systems evolve over time (like machine-learning models that continue to learn), regulators are looking into frameworks that evolve in real-time alongside the AI. For example, the FDA’s “Predetermined Change Control Plan” (PCCP) allows AI/ML-enabled devices to plan ahead for modifications and change protocols.
Finally, there’s Interdisciplinary Oversight. Compliance is no longer just about the law, but rather, intersects with technology, medicine, and ethics. So compliance leaders will need to understand AI and healthcare operations, too. Graduates of the Western State MLS program will learn how regulatory frameworks evolve, how to audit and govern emerging technologies, and how to interface across legal, clinical, operational, and technical domains.
Preparing for the Future: Compliance in the Age of AI
Building Smarter Governance
Compliance in the AI era starts with rethinking how compliance fits into healthcare, and what oversight even means. Hospitals are already experimenting with cross-disciplinary teams that pull in compliance officers, lawyers, clinicians, IT staff, and data scientists. These teams scrutinize every new AI tool, asking questions like: Is the data fair? Is it secure? Can anyone actually explain how the model reaches its answers? The goal is to make sure that the innovation introduced can be trusted.
Developing AI-Literate Compliance Leaders
The next step is education. Today’s compliance professionals will need to understand the technology operating under the regulations as well. That means learning how algorithms learn, where bias can slip in, and how to verify claims of transparency and accuracy.
More and more healthcare systems are already partnering only with vendors that can show how their models were trained and tested to prove their AI tools perform safely and equitably. That’s where Western State’s MLS in Healthcare Compliance comes in. The program prepares students to evaluate these new technologies through legal, ethical, and operational lenses.
Ultimately, the key to success in this new landscape is adaptability. As AI evolves, regulations will also change to keep up. The most successful leaders will be the ones who can adapt quickly and think on their feet and across disciplines while keeping innovation ethical, safe, and human-centered.
Innovation Meets Oversight: A Look Forward
The future of AI in healthcare will be shaped by how we govern it. Balancing innovation and regulation is an ongoing conversation about technology, ethics, and law. Making real headway in this realm will depend on systems being flexible and forward-thinking, and professionals who can think across disciplines to keep AI responsible as it evolves.
For healthcare organizations, the takeaway is clear: the advent of AI brings with it a paradigm shift. And for professionals considering the next step in their career, the intersection of law, healthcare, and emerging tech is a fertile ground. At Western State College of Law, our MLS in Healthcare Compliance prepares you to take on that ground—helping ensure that as technology accelerates, the safeguards accelerate too.
So if you’re looking at how to future-proof your healthcare compliance career, or how to position your organization for the next wave of innovation, you might ask yourself: Do I have the skills to speak the language of law and AI? Do I have the perspective to translate regulatory frameworks into operational practices? Because in an AI-enabled health-care system, that translation work is exactly where the value resides.
Interested in leading the next wave of healthcare innovation? Learn more about Western State College of Law’s MLS in Healthcare Compliance and how you can apply today.