There is a common expectation that if we or our family members become ill, our doctor will be able to treat us. For Americans suffering from a rare disease, that is often not the case. According to the National Organization for Rare Disorders (NORD), an estimated 10% of all Americans — or approximately 30 million people — half of whom are children, are affected by rare diseases.[1] Of the more than 10,000 rare diseases identified, less than 5% have approved treatments available.[2] Additionally, patients suffering from conditions that may not necessarily be considered rare — including many cancers — may either have no approved treatment options available or may have exhausted all approved treatment options. For both groups, participating in a clinical trial may be their only hope of therapy for their disease.
One of the most challenging aspects of clinical research is matching patients to clinical trials in which they may be eligible to participate. The process of reviewing medical records and comparing those records to clinical trial protocols to determine if the patient may be eligible for the clinical trial can be tedious and time-consuming. Countless patients give up on the process and never find or enroll in a clinical trial. In addition to the missed opportunity for patients, clinical trial enrollment goals are impacted, which may slow research and the discovery and approval of novel therapies. Recent advances in the use of large language models (LLMs) has led to the development of artificial intelligence (AI) tools to expedite and streamline the clinical trial matching process.
In November 2024, the National Institutes of Health (NIH) announced the development of an AI algorithm to match potential participants to clinical trials and stated they “… anticipate that this work could make clinical trial recruitment more effective and help reduce barriers to participation for populations underrepresented in clinical research.”[3] The use of similar tools has begun to spread amongst academic medical centers and other clinical research organizations (CROs). On Jan. 8, 2026, Mount Sinai’s Tisch Cancer Center announced the launch of an oncology-specific AI platform to help cancer patients find a clinical trial.[4] Other institutions have made similar announcements, among them the Cleveland Clinic in August 2025, which has partnered with a technology company to use their LLM-based platform to identify patients who may be eligible for clinical trials.[5] Mass General Brigham announced the spinout of a company founded by some of its researchers who developed an AI screening tool for participants seeking a clinical trial.[6]
These announcements highlighted the benefits of using AI to improve, expand, and accelerate clinical trial recruitment. They also made clear that the tools were assessed and evaluated prior to implementation to help ensure the matching process is accurate before being put into use. One key factor to consider is whether the AI tool can be generalized to screen for multiple clinical trials at once. This will require normalizing clinical trial eligibility criteria to match electronic medical record (EMR) fields and then building the AI tool to compare the EMR fields to the eligibility criteria for multiple trials.
Organizations should maintain human interaction with potential participants. Continued honesty and transparency about the investigational nature and inherent uncertainty of clinical trial treatment outcomes will ameliorate the risks of providing false hope and therapeutic misconceptions to an understandably desperate group of potential participants.
Since AI tools have the potential to review medical records and clinical trial protocols more quickly and efficiently than humans, these tools will decrease the eligibility-screening time, speeding up the enrollment process and enabling participants to begin the clinical trial sooner. The use of these tools will be a boon to patients with rare diseases and those with no approved treatments. These AI tools will also benefit pharmaceutical and medical device manufacturers, CROs, and institutions engaged in research. We anticipate there will be significant commercial opportunities in the drug and device development ecosystem, stemming from the application of AI tools to the challenge of matching patients to studies. These commercial activities will not be without risks, including patient privacy and anti-kickback concerns. Additionally, we expect that Institutional Review Boards (IRBs) will need to consider the impact on participants of any research-related process that utilizes AI. However, with careful consideration in building the AI tool to enable screening for multiple trials at once and with thorough review and oversight by IRBs and Research Privacy Boards to ensure participants’ privacy and the confidentiality of their records is maintained, the use of AI tools in matching potential participants to clinical trials could revolutionize the industry.
© Copyright 2026. The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC., its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.
[1] National Organization for Rare Disorders, Rare Disease Facts and Statistics, https://rarediseases.org/understanding-rare-disease/rare-disease-facts-and-statistics/
[2] National Organization for Rare Disorders, Fact Sheet, https://rarediseases.org/wp-content/uploads/2025/12/Rare-Disease-Fact-Sheet-V2-1.pdf
[3] National Institutes of Health, “NIH-developed AI algorithm matches potential volunteers to clinical trials” (November 18, 2024), https://www.nih.gov/news-events/news-releases/nih-developed-ai-algorithm-matches-potential-volunteers-clinical-trials
[4] Naomi Diaz, “Mount Sinai launches AI platform for clinical trial matching” Becker’s Health IT, (January 8, 2026) https://www.beckershospitalreview.com/healthcare-information-technology/innovation/mount-sinai-launches-ai-platform-for-clinical-trial-matching/?origi%E2%80%A6
[5] Andera Pacetti and Alicia Reale-Cooney, Cleveland Clinic Newsroom (August 27, 2025) https://newsroom.clevelandclinic.org/2025/08/27/cleveland-clinic-accelerates-clinical-trial-recruitment-with-roll-out-of-dyania-healths-artificial-intelligence-platform-across-health-system
[6] Ryan Jaslow, Mass General Brigham Newsroom (December 12, 2025) https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/aiwithcare-mass-general-brigham-spinout-new-company#:~:text=Through
