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OpenAI and FDA Talks Signal AI Revolution in Drug Evaluation: What It Means for Healthcare

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The US Food and Drug Administration (FDA) is engaging in active discussions with technology company OpenAI as part of a broader push to modernize drug evaluation with artificial intelligence. According to recent reporting by Wired, such collaboration could mark a pivotal shift in how new medicines are reviewed—potentially reducing the time it takes to bring life-saving drugs to market, and setting global trends that are closely watched in Thailand and across Asia Wired.

For Thai readers, the FDA’s move to consider AI as a tool for accelerating drug review carries enormous significance. New drugs typically require a decade or more to clear regulatory hurdles worldwide, including in Thailand, resulting in long waits for patients in need of innovative treatments. If advanced AI can streamline this process abroad, Thailand’s own regulatory authorities may face increasing pressure to adopt similar technology, especially as the country strives to maintain its reputation as a medical tourism hub and regional leader in healthcare.

The current discussions between the FDA and OpenAI revolve around projects referred to as “cderGPT” (after the FDA’s Center for Drug Evaluation and Research) and “Research GPT.” While no agreements have been finalized, sources reveal that a specialized OpenAI team has met with FDA officials on multiple occasions. The talks are led by the FDA’s first Chief AI Officer and have involved counterparts from the US Department of Health and Human Services as well as other government technology advisors Wired.

Significantly, the FDA’s commissioner recently commented publicly on social media about the urgency of modernizing drug evaluation with AI: “Why does it take over 10 years for a new drug to come to market? Why are we not modernized with AI and other things? We’ve just completed our first AI-assisted scientific review for a product and that’s just the beginning.” Although OpenAI itself has declined to comment, the FDA’s apparent openness marks an important departure from its historically cautious approach to digital disruption in healthcare.

Industry experts are watching closely. The former FDA commissioner noted that while review teams have employed AI for years, the latest efforts could expand its role from limited tasks to more comprehensive decision support. “It will be interesting to hear the details of which parts of the review were ‘AI assisted’ and what that means,” he said to Wired. He also emphasized that AI’s value could go far beyond just speeding up approval—by helping to modernize all facets of regulatory science.

Supporters like the CEO of Genialis, a precision oncology company, argue for the automation of “low-hanging fruit” in the approval pipeline—such as checking application completeness—to provide immediate efficiency wins. However, they also caution about the potential pitfalls. “There should be policy guidance around what kind of data is used to train AI models and what kind of model performance is considered acceptable,” one expert said, warning that AI must be carefully validated to avoid errors or “hallucinations”—when chatbots generate inaccurate information that could compromise regulatory judgments.

An ex-FDA evaluator who trialed ChatGPT as a clinical decision tool cautioned, “Who knows how robust the platform will be for these reviewers’ tasks,” highlighting the significant risk of relying on AI-generated documents in high-stakes decisions.

The FDA already offers “fast track” and “breakthrough therapy” designations to accelerate review for promising drugs meeting unmet needs or showing substantial clinical improvement. Currently, a full drug review averages about a year, but most drug candidates succumb to failure before this stage. The hope is that AI could not only shorten the final review period but also optimize earlier stages, supporting everything from data triage to literature analysis Wired.

Thailand’s regulatory landscape has closely followed US and European models, and the Department of Food and Drug Administration routinely references international benchmarks in adapting its review practices. Thai hospitals—particularly those aiming to attract international patients—would benefit from any reduction in time-to-approval for advanced therapies. If the US FDA adopts AI in key clinical review processes, it could provide a model for the Thai FDA and other regional regulators to follow.

However, there are cultural and practical considerations unique to Thailand. Thai authorities remain cautious regarding data privacy and cyber-risk, mindful of several high-profile healthcare data breaches in recent years. Any movement toward AI integration would require robust public engagement, technical upskilling, and clear guidelines on how AI models are trained and used in high-stakes decisions regarding patient safety.

Historically, Thailand has distinguished itself as an early adopter of medical AI in certain domains, such as radiology and hospital management. Several leading Bangkok hospitals use AI for diagnostic imaging, while the Ministry of Public Health has established pilot projects for AI-assisted disease screening. Yet regulatory use of AI—especially for deciding on safety and efficacy of new drugs—would represent a significant departure from current practice PubMed.

Globally, pharmaceutical AI has accelerated drug discovery and preclinical research, but final regulatory signoff remains a human-dominated preserve. The pace of AI adoption within official drug approval mechanisms will depend on government risk tolerance, availability of local expertise, and harmonization with international standards. If the United States proceeds, the ripple effects will likely extend to ASEAN regulators, including Thailand.

Industry analysts anticipate several future developments. Beyond initial AI adoption for document review, future versions of AI systems could be tasked with interpreting clinical trial data or detecting inconsistencies in study methodology. More sophisticated applications might include AI-powered simulations that predict patient outcomes or flag potential health risks undetectable by humans. However, increased reliance on AI also raises concerns about accountability and potential bias, echoing global debates around automated decision-making in critical sectors.

The practical impact for Thais—especially patients, physicians, and pharma companies—could be profound. Faster drug approval could mean quicker access to innovative therapies for diabetes, cancer, and other pressing health concerns. Thai drug manufacturers aiming for US or EU market access might also benefit if AI-enabled reviews open new opportunities for international collaboration or harmonized regulatory pathways. However, vigilance is needed to ensure that automated processes are transparent, fair, and responsive to local healthcare priorities.

The recent FDA-OpenAI talks signal that the global drug approval landscape is on the cusp of radical change. For Thailand, the lesson is clear: preparing for the AI revolution in regulatory science will mean investing in digital capacity-building, strengthening data governance, and fostering robust cross-sector dialogue about the right balance between speed, safety, and social trust.

For Thai readers who want to ensure their voices are heard in shaping the future of medical regulation, now is the time to educate themselves on the promises and pitfalls of AI in healthcare. Engage with public consultations, seek out reliable news on technology and health, and ask hospital or national authorities how AI is already being used in local care settings. By remaining informed and proactive, Thais can help guide this technological wave to benefit society as a whole.

Sources: Wired, PubMed

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Medical Disclaimer: This article is for informational purposes only and should not be considered medical advice. Always consult with qualified healthcare professionals before making decisions about your health.