The integration of artificial intelligence (AI) into the U.S. Food and Drug Administration’s (FDA) drug approval process would allow new medications to be approved “very, very quickly,” according to a recent statement made by a prominent U.S. presidential candidate. The assertion, made at a high-profile live event, has ignited renewed debate about the potential of AI to revolutionize pharmaceutical regulation—raising questions of safety, ethics, and global implications, including for Thailand’s own drug approval landscape.
The context of this claim comes at a time when healthcare systems worldwide are grappling with inefficiencies in how new treatments reach patients. In the United States, where the FDA serves as the regulatory gatekeeper for drug approvals, the path from laboratory breakthroughs to pharmacy shelves remains a lengthy, complex process. This is compounded by mounting public demand for faster access to life-saving therapies, particularly seen during the Covid-19 pandemic, which exposed both the strengths and flaws of existing regulatory frameworks (FDA Approval Process). Against this backdrop, the prospect of harnessing AI to automate and accelerate drug evaluation is highly appealing—and controversial.
According to published coverage on Gizmodo, the U.S. presidential candidate argued during an interview that AI, once implemented within the FDA’s review system, could dramatically cut down approval timelines. “We can have drugs approved very, very quickly if AI is in the loop,” the candidate insisted, suggesting that automation could enhance both speed and accuracy. The remarks stirred immediate interest among tech enthusiasts and skepticism from medical professionals wary of potential dangers.
Drug approvals traditionally require a rigorous, multistage process that includes laboratory testing, animal studies, and multiple phases of human clinical trials. Each step necessitates meticulous scrutiny, peer review, and usually years—sometimes over a decade—before any new compound reaches consumers (Nature Review: Drug Approval Timelines). Proponents of AI note that advanced algorithms can rapidly process vast datasets, identifying patterns and safety signals that might elude even the most diligent human assessors. In recent years, machine learning has shown promise in drug discovery, side-effect prediction, and even optimizing clinical trial design.
Yet, regulatory authorities remain cautious. While AI-powered analytics could help flag potential risks, there is concern over algorithms introducing new biases or failing to detect less obvious dangers. As noted in a 2024 review published in the medical journal JAMA, “AI recommendations must be validated by rigorous clinical evidence before being used as the sole basis for approval decisions” (JAMA AI in Medicine Special Issue). The use of AI for regulatory approvals is still in nascent stages worldwide, typically limited to supporting rather than replacing human judgment.
Senior scientists at the U.S. FDA have previously stated that while AI may assist in scanning scientific literature, flagging safety signals in post-market surveillance, and streamlining administrative review, the “art of regulatory science” involves nuanced ethical deliberations and context-specific decisions that algorithms are ill-equipped to handle fully (FDA AI Guidance).
Thailand’s Food and Drug Administration, which faces similar challenges of balancing expedited drug access with patient safety, is observing these developments closely. According to a leading official at the Thai FDA, “AI-based decision support could eventually be a valuable tool, but regulators must first ensure systems are transparent, free from bias, and thoroughly tested for reliability.” Thailand’s regulatory system, modeled after international best practices, is actively investing in digital infrastructure, as seen in the government’s push towards ‘Smart Health’ initiatives (Thai FDA Digital Transformation).
Thailand’s pharmaceutical industry, which is an important hub for generic drug manufacturing and regional clinical trials, may see both opportunity and risk if global standards shift toward greater use of AI. There is hope that AI could address the chronic shortage of expert reviewers and automate complex analyses of clinical trial data, which can help fast-track treatments for diseases that disproportionately affect Southeast Asia, such as dengue fever, thalassemia, or endemic cancers.
Cultural and historical factors also shape local perspectives. Like many countries in the region, Thailand has a strong tradition of seeking expert human judgment before introducing new health interventions, rooted in a public wariness of untested medical innovations. Events in recent years, such as concerns around the rapid rollout of certain treatments or the spread of vaccine misinformation, have underscored the need for robust, transparent regulatory checks.
Critics warn that entrusting drug approvals to AI could shift accountability away from named experts and towards opaque systems that the public may not understand or trust. “Algorithms are only as good as the data and assumptions they’re built on,” noted a Bangkok-based clinical pharmacologist, adding that “any mistakes in programming or data collection could have catastrophic effects if undetected.” Other experts express concern over cybersecurity, data privacy, and the potential for AI’s decisions to be manipulated by commercial interests.
Still, there are signs of a measured path forward. International regulatory alliances are exploring so-called “explainable AI”—algorithms whose decisions can be audited and validated by human reviewers. Pilot projects in Europe, Japan, and even some U.S. states are experimenting with AI to triage routine applications, reserving final judgment for experienced human panels (EMA: AI in Medicines Regulation). Thai regulators are taking part in regional training programs to build their own AI literacy, recognizing the need for capacity-building in this fast-changing field.
Looking to the future, the implications of AI-assisted drug approval for Thailand are wide-ranging. If AI can safely accelerate new medicines to patients in need—while maintaining high standards for safety and efficacy—it could transform public health. Faster approvals might also boost local industry competitiveness and encourage investment in Thai clinical research. However, hasty adoption without robust safeguards could compromise patient safety, undermine public trust, and provoke regulatory or legal backlash.
For Thai consumers, the prospect of “AI at the FDA” reinforces the need to stay informed and engaged in health policy debates. Public demand for both safer and quicker access to innovative treatments must be balanced, not at the cost of diminished oversight. Regulators and healthcare professionals are urged to invest in digital literacy, transparency, and ongoing dialogue with international counterparts, ensuring that Thailand’s regulatory system is both modern and resilient.
Ultimately, while AI holds great promise for revolutionizing pharmaceutical regulation, expert consensus suggests its role should remain that of a powerful assistant—not an unchecked authority. Thai policymakers, researchers, and the public should monitor global developments closely, participate in evidence-based trials of new regulatory tools, and insist on meaningful transparency and accountability at every stage. For now, the vision of AI instantly “green-lighting” new drugs remains more science fiction than policy—at least until rigorous pilot programs and public consensus catch up with the enthusiastic claims.