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As we recognize National Cancer Research Month this May, it's important to acknowledge the continued need to strengthen the clinical trial workforce and to explore solutions that can expand access to potentially lifesaving clinical trials.
Each May, National Cancer Research Month offers a moment to celebrate the achievements of oncology clinical research while also recognizing the critical work still ahead. Thanks to numerous successes in preclinical cancer research, the field has reached a pivotal juncture where progress is increasingly constrained not by discovery, but by the capacity to conduct and enroll patients in clinical trials. To fully realize the promise of these scientific discoveries and ensure patients have access to potentially life-changing treatments, the clinical trial workforce must be strengthened. As oncology data expands at an unprecedented rate, clinicians and research staff face escalating demands. This burden is felt acutely in community oncology settings, where resources are often limited and operational pressures are high.
A key challenge facing community oncology sites is the complex, time-consuming process of matching patients to appropriate clinical trials. Oncologists and their teams are expected to evaluate thousands of patients against the eligibility criteria for hundreds of active trials each year. This task requires deep clinical knowledge, attention to detail, and, traditionally, labor-intensive manual chart reviews. Given these constraints, it’s not uncommon for patients to miss out on potentially lifesaving clinical trials—not because they are ineligible, but because the opportunity was not recognized in time.
At the recent 46th Annual Meeting of the Society for Clinical Trials (SCT) in Vancouver, a forward-looking session focused on a potential solution: the use of artificial intelligence (AI) to support and streamline patient-trial matching in real-world clinical environments. This technology offers a scalable, intelligent way to reduce workload and expand access to cancer clinical trials in the community setting.
In place of overhauling active IT systems and infrastructure, sessions at SCT highlighted AI solutions, such as PRISM, that are designed to integrate within existing clinical workflows, rather than altering or adding to them. PRISM is embedded directly within commonly used electronic health record (EHR) systems. 4 core AI-powered applications were highlighted:
Together, these capabilities improve efficiency, support equity, and create a scalable model for trial recruitment that fits community oncology's complex landscape. For trial sponsors, the benefits are equally compelling: increased enrollment, reduced timelines, and broader, more representative data.
A known challenge of generative AI is the potential for “hallucinations,” or inaccurate suggestions. To mitigate this risk, the SCT session emphasized transparency: each AI-generated match is linked directly to the patient’s medical record, with clear explanations for why a patient does or does not meet inclusion/exclusion criteria. This enables real-time, human-in-the-loop validation, reinforcing both trust and accountability in patient care.
Recognizing that successful adoption of new tools requires human expertise and support, the session emphasized an end-to-end implementation model to support the rollout of AI tools like the PRISM platform. AI solutions can (and should) be deployed alongside clinical experts who ensure that the tool is a good fit for the realities of day-to-day clinical practice. The human component of this team includes the following:
These professionals work closely with site staff, validating AI findings, synthesizing feedback for improvement, and ensuring seamless integration into existing clinical operations. This model acknowledges that while AI can augment human decision-making, expert oversight is essential, especially in oncology where the stakes are high and every detail matters.
AI won’t replace the clinical research workforce, but it can empower it. By reducing administrative burden and surfacing the right information at the right time, AI allows clinicians to focus on what matters most: patient care and clinical decision-making.
As we reflect on National Cancer Research Month, it is clear that innovation in how we support the workforce is just as important as scientific innovation itself. The thoughtful integration of AI into community oncology practices can promote increased access to clinical trials and a more equitable research paradigm.
As the frontline of cancer care, community oncology practices deserve solutions that meet them where they are—solutions that work with their existing systems, augment their capabilities, and help them keep pace with the ever-evolving clinical research landscape. By embracing validated, human-centered AI, we can take a major step toward a future where all patients have access to the promise of clinical trials.
