SAN FRANCISCO, Jan. 26, 2026 — Drugmakers are turning to artificial intelligence to shorten clinical trials and speed regulatory submissions at a time when finding new medicines remains difficult. Executives say the technology is cutting administrative and operational work that has slowed development for years, leaving scientists with more time for evaluation and judgment.

At the annual JP Morgan Healthcare Conference, executives described how AI is being deployed in functions that once relied heavily on manual review and outsourced labor. These include selecting trial sites, recruiting patients, monitoring performance during studies, and assembling regulatory filings that often run into thousands of pages. While AI has yet to deliver consistent breakthroughs in drug discovery, companies say it is already changing how trials are run and how submissions reach regulators.

Faster Trial Setup and Enrollment

Clinical trials often face delays before enrollment even begins, driven by site selection challenges and slow patient recruitment. Drugmakers say AI systems can now analyze large volumes of historical data to predict which trial locations are most likely to perform well and which patients meet strict eligibility criteria. That capability allows decisions to be made earlier and with fewer revisions, reducing idle time at the start of studies.

Novartis said AI helped compress a site selection process that previously took several weeks into a single two-hour meeting. The system was used during a cardiovascular trial involving about 14,000 patients for the cholesterol drug Leqvio. By identifying higher-performing sites sooner, the company completed enrollment with only 13 patients left to recruit, shortening timelines without weakening oversight.

Site Selection Guided by Data: Executives said AI tools assess past trial performance, investigator experience, and enrollment history to rank potential sites. This allows companies to focus resources on locations more likely to deliver results on schedule. In large global trials, where dozens of countries may be involved, this process reduces the risk of uneven progress across regions.

By spotting potential bottlenecks early, companies can adjust staffing and logistics before delays spread. That reduces the need for midtrial interventions, which are often costly and disruptive.

Improving Patient Recruitment and Retention: AI is also being used to improve how patients are identified and supported throughout trials. Systems can scan medical records and demographic data to match eligible patients more quickly, while automated outreach tools keep participants informed about schedules and requirements.

Some companies report that better communication has helped limit dropout rates, a common problem that can force trials to recruit additional patients. Keeping participants engaged allows studies to stay on track and reduces the likelihood of timeline extensions.

Regulatory Filings Move Faster

Preparing submissions for regulators has long been one of the most resource-intensive parts of drug development. A single filing may include clinical data, manufacturing details, and safety reports that must remain consistent across multiple versions and jurisdictions.

Companies such as AstraZeneca, Roche, and Pfizer now rely on AI to manage this workload. Large language models assist with drafting sections of submissions, cross-checking data, and tracking changes across documents. These systems help ensure that filings submitted in different countries remain aligned, even when regulatory requirements differ.

Executives said this shift has reduced reliance on external contractors who once handled much of the documentation work. Internal staff can now oversee submissions more directly, while AI handles repetitive checks and formatting tasks.

Investors Back Operational Tools

Venture capital firms are directing funding toward startups focused on operational challenges rather than drug discovery. Jorge Conde of Andreessen Horowitz described this area as the messy middle of drug development, where inefficiencies can delay progress and inflate costs.

One startup, Alleviate Health, uses AI to manage patient outreach, screening, education, and scheduling. By improving communication and follow-through, the system helps trials retain participants and avoid costly disruptions. Investors say such tools address problems with immediate financial consequences for drugmakers.

Analysts note that results vary depending on how well AI systems are integrated into existing workflows. Some companies may see time savings within months, while others may need longer to measure results.

Industry Adoption Widens

Large pharmaceutical companies are also forming partnerships to extend their use of AI. Eli Lilly is working with chipmaker Nvidia to apply advanced computing to trial operations, while Teva Pharmaceutical Industries is using AI to improve efficiency across development and launch preparation.

Teva Chief Executive Richard Francis said scientific research is only one part of bringing a drug to market. Other processes must be made as efficient and as small as possible to avoid unnecessary delays. AI, he said, plays a role in modernizing those systems and reducing friction.

Consulting firm McKinsey has estimated that autonomous AI systems could lift productivity in clinical development by as much as 45 percent over five years. That expectation has encouraged companies to invest early, even as standards and oversight continue to evolve.

Artificial intelligence is not replacing scientists or regulators, according to industry executives. Instead, it is changing how routine tasks are handled, allowing human expertise to focus on judgment and review. While the search for new treatments remains difficult, drugmakers say faster trials and filings can shorten the path from laboratory to patient.