AI is changing the way hospitals and other healthcare institutions work. It helps doctors read scans, tracks patient information, and handles billing automatically. In many sectors, technology has made things faster and more efficient. It also raises new legal and moral problems that the healthcare industry is still figuring out how to deal with.
Steven Okoye, a corporate and healthcare attorney based in New York, says healthcare organizations must strengthen compliance frameworks as they adopt new tools. With more than 7 years of experience in transactional law, healthcare regulation, and corporate governance, Okoye has worked with teams on the practical side of risk management in complex environments.
“AI brings opportunity,” he says, “but it also introduces accountability concerns that are not always fully understood at the start.”
The Expanding Role of Artificial Intelligence
Artificial intelligence is now used across both clinical and administrative functions. In hospitals, algorithms can analyze medical images, predict patient outcomes, and flag high-risk cases. In offices, AI programs manage scheduling, insurance claims, and patient communication.
The Food and Drug Administration (FDA) regulates certain types of AI under its Software as a Medical Device category. Developers must prove their software’s safety and effectiveness before release and continue monitoring performance after deployment.
However, FDA oversight does not extend to many other systems. Predictive tools for billing or staffing decisions often fall into regulatory gray areas. Okoye notes that this lack of uniformity makes internal compliance policies especially important. Without them, hospitals risk uneven accountability across departments.
The Rise of State-Level Oversight
Recently, several states have moved to establish rules around algorithmic transparency and accountability. California, New York, and Colorado have proposed or enacted laws requiring companies to explain how AI systems make decisions and to test for bias in their data sources.
This approach creates a patchwork of standards. Healthcare systems operating across multiple states must track differing requirements and adapt accordingly. Okoye explains that organizations with consistent internal policies are better equipped to meet both state and federal expectations. He often recommends that health systems apply the most rigorous standard across all operations to avoid future conflicts.
Legal and Ethical Risks
Bias, mistake, and patient safety are the three main types of legal hazards that AI in healthcare poses.
When training data only includes a small number of people, it can cause bias, which can lead to wrong or unfair outcomes. In clinical settings, this might mean misdiagnoses or uneven treatment recommendations. Regulators have begun paying attention to how training data influences outcomes, especially when patterns appear to disadvantage certain groups.
An error is a mistake in how AI systems process or interpret information. These issues can stem from flawed design, incorrect data input, or overreliance on automated recommendations. Determining liability can be difficult. Questions often arise about whether responsibility lies with the developer, the healthcare provider, or the institution that approved the tool’s use.
Patient safety remains the core concern. AI-driven decisions must be transparent and verifiable. If a physician or hospital cannot explain how an algorithm reached a conclusion, accountability becomes complicated during audits or investigations. Okoye stresses that human oversight must always remain part of the process, especially when technology is used to inform medical care.
Strengthening Compliance Programs
In the past, healthcare compliance programs have mostly focused on privacy, billing, and stopping fraud. We need to have a bigger view of artificial intelligence.
Okoye’s expertise in running legal and regulatory operations indicates that having a plan and writing things down is important. A good compliance program should make it obvious where data originates from, how algorithms are checked, and how vendors are monitored. Automated record-keeping systems can help keep things clear and consistent amongst departments.
Regular audits of AI systems should test for accuracy and fairness. Vendor contracts should include requirements for data disclosure and ongoing performance reviews. Cross-departmental collaboration among legal, IT, and clinical staff ensures that compliance is not treated as a single department’s responsibility but as an organizational standard.
What Regulators Are Watching
Several federal authorities are now looking into how AI fits into the laws we already have. The FDA’s Digital Health Center of Excellence is still developing guidelines for adaptive algorithms that improve over time through machine learning. The Department of Health and Human Services is looking at how these tools work with HIPAA privacy rules. The Federal Trade Commission has issued reminders that marketing claims about AI accuracy must be truthful and supported by evidence.
At the state level, attorneys general are also reviewing how health organizations collect and use patient data. Regulators are particularly concerned about the potential for automated systems to unintentionally discriminate against or mislead consumers. Healthcare organizations are expected to maintain documentation showing how their AI systems are tested, updated, and governed.
Preparing for Future Rules
Experts expect federal and state regulations on AI documentation and reporting to expand in the coming years. Healthcare organizations that take early steps toward compliance will be better prepared when these rules arrive.
Okoye encourages teams to start by identifying every area where AI is already being used. From diagnostics to patient outreach, knowing where automation exists is the first step toward accountability. Staff training should follow. Everyone involved in care or operations should understand what these systems do and how to respond if something goes wrong.
Clear communication between departments and vendors is also essential. Documenting approval processes, data sources, and performance results builds a trail of responsibility that regulators value. These habits also strengthen patient trust.
Balancing Progress and Responsibility
Artificial intelligence will continue to expand in healthcare. It has the power to improve diagnosis, streamline workflows, and make care more efficient. But without proper oversight, it can also create new risks and uncertainty.
Steven Okoye thinks that success is all about balance. Innovation should continue to progress, but compliance must accompany it. Healthcare companies can safely use AI and maintain patients’ trust by adopting systems that integrate openness, governance, and accountability.
As technology improves, the question will shift from whether to employ AI to how to do so responsibly. The most responsible companies will see compliance as a way to move forward, not as a roadblock.
