Can AI trial matching close the gap in cancer drug access?

Artificial intelligence is rapidly changing how cancer patients gain access to potentially life-saving treatments, particularly by transforming the way clinical trial enrollment is managed. Companies like Tempus AI Inc. (NASDAQ: TEM), TrialJectory, and CureMatch are developing AI-driven platforms that automatically match patients to clinical trials based on their unique medical and genomic profiles. These tools aim to close longstanding disparities in access—especially for patients with rare cancers, from rural communities, or without access to leading cancer centers.

The push toward AI-based trial matching reflects broader systemic challenges in oncology: a growing number of precision therapies, increasingly complex trial inclusion criteria, and an urgent need to increase trial diversity. Traditional referral processes are no longer sufficient. As new cancer drugs become more genomically targeted, the ability to rapidly identify eligible patients at scale has become essential—not just for individual outcomes but also for the speed and success of drug development programs.

How Does AI Improve the Clinical Trial Matching Process?

AI algorithms bring scale, accuracy, and speed to a task that has historically been manual and error-prone. In most oncology practices, clinicians or coordinators have had to sift through dozens of trial protocols and eligibility documents to determine whether a patient might qualify. This process, often fragmented across spreadsheets or EHRs, has led to under-enrollment in trials and missed opportunities for patients.

AI trial-matching platforms such as those developed by Tempus AI Inc. (NASDAQ: TEM) now scan structured and unstructured patient data—including electronic health records, pathology reports, and genomic sequencing profiles—to deliver real-time trial eligibility matches. Through its flagship TIME Trial® Program, Tempus has enabled providers to rapidly enroll patients into appropriate clinical trials by linking molecular profiling results with available trials across hundreds of partner institutions.

Similarly, New York-based TrialJectory uses natural language processing and proprietary algorithms to analyze unstructured clinical notes and cross-reference them with trial databases, ensuring no patient is excluded due to lack of human bandwidth. The result is a faster, more comprehensive, and more equitable match process. In an environment where oncology trials are growing more biomarker-specific, this automation is proving critical for matching patients who might otherwise be invisible to the system.

Why Does AI Trial Matching Matter for Underserved Patients?

The use of AI in trial matching is not only a technological advancement—it has direct implications for healthcare equity. Historically, patients in rural or community settings have been underrepresented in clinical research due to a lack of access to trial sites, limited referral pipelines, and logistical burdens such as travel and cost.

Tempus’s TIME Trial® infrastructure, by offering centralized IRB approvals, mobile phlebotomy, and remote enrollment capabilities, has allowed community oncology practices and smaller hospitals to participate in the research ecosystem. As a result, patients who once had no access to experimental therapies can now be matched to active trials within their geographic and clinical contexts. These models also accelerate patient onboarding by automating site initiation, thereby reducing trial startup time from months to weeks.

A 2025 study presented at an ACCC oncology workforce forum showed that AI-enhanced matching tools increased patient accrual rates by up to 60% in community clinics across the U.S. By enabling non-academic settings to serve as active trial nodes, AI platforms are rebalancing the geography of cancer research, ensuring trials reflect real-world diversity—not just patients near major academic medical centers.

What Are the Challenges in Scaling AI Trial Matching?

Despite the promise, challenges persist. First, the effectiveness of AI-based matching is highly dependent on data quality and interoperability. Variations in EHR formats, incomplete medical records, and genomic data silos can reduce algorithmic accuracy. To address this, companies like Tempus have built cloud-native infrastructure that integrates directly into EHR platforms like Epic, reducing friction in real-time data extraction.

Data privacy and compliance are also critical. Trial-matching platforms operate under HIPAA and GDPR frameworks and must secure patient consent before data is used for matching. Companies are investing heavily in secure consent platforms and anonymization techniques to enable scale without compromising trust.

Lastly, institutional inertia remains. Many oncologists still rely on personal knowledge of trials or investigator-initiated contacts. Training clinical staff to trust and use AI-generated trial suggestions—especially when these involve decentralized or virtual trials—is an ongoing behavioral shift that health systems must lead from the top.

What Role Are Companies Like CureMatch and IBM Playing?

Beyond Tempus and TrialJectory, other AI-first platforms are shaping the landscape. San Diego-based CureMatch focuses on combination therapy matching using advanced genomic analytics. By analyzing the mutational landscape of a tumor, CureMatch’s algorithms recommend optimal drug combinations—including those in trials—with efficacy scores. This has been especially valuable for late-stage or treatment-resistant patients, many of whom have exhausted standard care options.

Meanwhile, IBM Watson for Oncology, although sunsetted commercially, played a foundational role in introducing AI into trial-matching logic through early deployments in Asia and the U.S. Lessons learned from Watson’s limitations—particularly its lack of flexibility and clinician distrust—have informed the more iterative and explainable models being adopted by Tempus, TrialJectory, and CureMatch today.

What Is the Investor and Institutional Sentiment Toward This Segment?

Investor interest in trial-matching startups has intensified since 2023, with both venture capital and strategic pharma partners seeking exposure to platforms that can accelerate trial enrollment. Tempus’s own IPO in Q2 2025 brought renewed attention to this segment, with its trial-matching solutions viewed as a differentiated growth lever compared to standard NGS labs. Analysts have highlighted the TIME Trial program as a “system-level enabler” with the potential to expand Tempus’s total addressable market by integrating biopharma, provider, and patient endpoints.

Similarly, TrialJectory has announced several undisclosed pharmaceutical collaborations, where sponsors use its algorithms to mine EHR data from non-traditional settings and match patients proactively. This B2B model has strong parallels with data-driven patient recruitment strategies seen in the U.S. COVID-19 vaccine trials.

Looking ahead, analysts believe AI platforms that offer closed-loop solutions—from eligibility to onboarding and eConsent to real-world data feedback—are best positioned to drive market leadership.

What Comes Next for AI in Trial Matching?

Future iterations of AI trial matching are expected to integrate real-world outcome data to refine eligibility criteria dynamically. For example, a patient deemed ineligible under static trial protocols might be flagged by an AI system for inclusion based on favorable comorbidity management, prior treatment response, or predictive benefit from investigational arms.

Several companies are also exploring multi-language AI assistants to increase accessibility for non-English speaking populations, which remains a key exclusion factor in global oncology trials. As sponsors and CROs push for faster accruals in a post-COVID world, platforms that can automate diversity, decentralization, and decision support simultaneously will likely dominate new study pipelines.

While still an evolving space, AI-based trial matching is no longer a fringe innovation. It is becoming a foundational layer of oncology operations, one that could significantly reduce inequities in access to advanced cancer therapies—particularly for patients who, until recently, were never given a seat at the clinical research table.