Our nation’s healthcare system is facing a pivotal moment and unprecedented challenges that demand bold, innovative solutions. Workforce shortages, clinician burnout, skyrocketing costs, and deep-seated inequities—particularly in rural and underserved communities—have created a crisis in care delivery. At the same time, artificial intelligence (AI) has become the key talking point as the closest thing to a panacea to enhance efficiency, accessibility, and equity.
Yet, the extent of AI’s reach depends on the environments in which it operates as well as how it is developed and deployed, highlighting a fundamental debate on whether we should push for regulation or opt for free-market-driven deregulation.
The Case for Regulation
Historically, regulation has been the cornerstone of public trust in healthcare. The establishment of the Food and Drug Administration (FDA) in 1906 played a critical role in ensuring the safety and efficacy of medicines and medical devices. Over the years, additional policies—such as Medicare’s Conditions of Participation, which hold hospitals to strict safety standards, and the National Institutes of Health (NIH) guidelines for clinical trials—have reinforced a culture of accountability. These safeguards have prevented harm, ensured ethical practices, and created a foundation of trust for patients and providers alike.
In the domain of AI, regulation could serve an essential purpose. Left unchecked, it could introduce risks ranging from biased decision-making to outright harm. For example, the Optum health algorithm—which was found to recommend less care for Black patients than white patients with similar health conditions—highlights how unregulated tech (and particularly AI) could perpetuate systemic inequities. Regulatory frameworks, by enforcing standards for transparency, accountability, and fairness, can mitigate such risks and ensure that AI benefits everyone.
Regulation also has the power to validate and enhance the trustworthiness of innovative technologies. The approval of IDx-DR, the first AI system authorized by the FDA to detect diabetic retinopathy, is a case in point. Its adherence to rigorous safety and efficacy standards demonstrated the company’s ability to meet critical healthcare needs, winning the hard fight for public trust.
Moreover, regulation can act as a catalyst for adoption by instilling confidence among clinicians, patients, and institutions. For example, Viz.ai, a platform that accelerates stroke detection using machine learning, was validated with an FDA-approvalthat paved the way for its widespread adoption in hospitals across the United States. In this sense, regulation isn’t about creating restrictions but creating a foundation of trust that allows innovation to proceed in a sensible way.
The Case Against Overregulation
Excessive oversight can stifle innovation, delay the deployment of life-saving technologies, and create disproportionate burdens for smaller companies. Startups with limited resources often struggle to navigate complex regulatory requirements, which can entrench the dominance of larger, more established players.
Drawn-out approval processes for tools like Butterfly iQ, an AI-powered handheld ultrasound device, serve as a case in point. While ultimately transformative, the delays in its regulatory pathway limited its immediate impact, particularly in underserved communities where such technologies are needed most.
In some cases, strategic deregulation has shown how loosening constraints can lead to rapid innovation and expanded access to care. COVID-19 is a powerful example. Temporary policy changes during the pandemic allowed telehealth platforms like Amwell and Babylon Health to scale quickly and meet critical needs. Babylon Health, for example, leveraged AI for symptom checking and virtual consultations that offered accessible healthcare options to underserved populations.
A Balanced Approach
The solution to this debate does not lie in choosing one approach over the other but in finding a balance that leverages the strengths of both regulation and deregulation. Regulation can ensure that AI tools are safe, effective, and equitable, while deregulation can foster innovation and reduce barriers to entry. Together, these forces can create an environment in which AI thrives responsibly.
Adaptive policy models offer a promising way forward. Regulatory sandboxes, for instance, allow companies to test new AI technologies in controlled environments and balance the need for oversight with the flexibility to innovate. Similarly, tiered oversight can differentiate between high-risk AI applications (such as diagnostic tools that warrant rigorous scrutiny) and lower-risk tools (like administrative automation that can be developed with more leniency).
Importantly, this balanced approach requires collaboration among regulators, innovators, and stakeholders across the healthcare continuum. Policymakers must remain agile and ensure that regulations evolve alongside technological advancements rather than lag behind them. At the same time, developers and companies must embrace transparency and accountability as foundational principles, recognizing that trust is not a barrier to success but a bridge to long-term adoption.
A Vision for the Future
AI has the potential to revolutionize healthcare by addressing its most pressing challenges, from alleviating clinician burnout to closing gaps in access and equity. But realizing this potential requires a regulatory environment that protects patients while encouraging innovation. It is not a matter of choosing between safety and speed, oversight and flexibility, or trust and progress. Instead, it is about harmonizing these priorities to build a healthcare system that leverages AI to deliver better outcomes for everyone, everywhere.
The future of healthcare depends on our ability to strike this balance. By combining the best of regulation and deregulation, we can create a system that meets today’s challenges while paving the way for tomorrow’s breakthroughs.
