MatchMiner
Overview
MatchMiner is an open-source platform developed by the Dana-Farber Cancer Institute that leverages artificial intelligence (AI) to connect cancer patients with suitable precision oncology clinical trials. It was launched in 2016 and initially used a rules-based engine to match patients based on structured genomic biomarkers and cancer type. The platform has since evolved to incorporate AI, enabling it to analyze unstructured data from electronic health records (EHRs) to extract crucial details like prior treatments, disease stage, and comorbidities. This allows for broader trial coverage, moving beyond just genomic eligibility to include criteria based on histology, prior treatment, and performance status. MatchMiner aims to accelerate trial enrollment, maximize potential trial options for patients, and streamline the patient-trial matching process, which has traditionally been manual and time-consuming. Studies have shown that MatchMiner can decrease the time to consent for precision medicine trials by a median of 55 days, and it has facilitated hundreds of patient enrollments. The platform is designed to be integrated into clinical workflows, providing clinicians with real-time access to trial matches and patient genomic reports.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered patient-trial matching
- Analysis of structured and unstructured clinical data
- Genomic trial matching
- Broad trial coverage (beyond genomics)
- Patient summaries for quick assessment
- Trial curation interface
- Oncologist dashboard
- Investigator view
- Integration with EHR and genomic sequencing projects
- Open-source platform
Use Cases
- Accelerating patient enrollment in precision oncology clinical trials
- Maximizing potential trial options for cancer patients
- Streamlining the identification of eligible patients for clinical studies
- Providing clinicians with real-time trial match information
- Supporting proactive outreach by trial investigators
- Facilitating collaboration and innovation in precision oncology research
What Physicians Need to Know
Leverage MatchMiner's patient-centric mode to quickly identify precision medicine trial options for your patients based on their genomic and clinical profiles. Utilize the AI-powered features to gain insights from unstructured EHR data, expanding the scope of potential trial matches beyond genomic criteria. For trial investigators, the trial-centric mode can efficiently identify eligible patients for your studies, accelerating recruitment. Remember that MatchMiner serves as a pre-screening tool, and further evaluation of a patient's readiness for a trial is still necessary.
MatchMiner is designed to integrate with institutional genomic and clinical data feeds, including Electronic Medical Record (EHR) systems. It is seamlessly integrated with DFCI's Profile next-generation sequencing project. The platform uses Clinical Trial Markup Language (CTML) to structure trial eligibility criteria and can be coupled with trial management systems for real-time trial status updates. Future enhancements may include support for imaging data, lab results, and integration with national trial registries.
Details
| Category | Drug Discovery & Research, Oncology AI |
| Pricing | Free — MatchMiner is an open-source and freely available platform. |
| Deployment | On-premise (behind institutional firewall) |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Unknown AI-estimated There is no information available to suggest that MatchMiner has FDA clearance. As an open-source platform for matching patients to clinical trials, it likely falls outside the scope of requiring FDA clearance. |
| Integrations | |
| EHR | Not specified |
| Specialties | Hematology, Oncology |
What the Web Says
MatchMiner is an open-source computational platform developed by the Dana-Farber Cancer Institute to match cancer patients with precision medicine clinical trials based on genomic and clinical data. It aims to accelerate trial enrollment and increase patient access to novel therapies. The platform has demonstrated success in reducing the time to consent for clinical trials by an average of 55 days and has facilitated a significant percentage of precision medicine trial consents at Dana-Farber. A newer version, MatchMiner-AI, launched in February 2026, utilizes large language models (LLMs) to process unstructured clinical notes and match patients to a broader range of trials, moving beyond solely genomic criteria.
Overall: PositiveStrengths
- Open-source platform, making it adaptable for other institutions.
- Reduces time to consent for clinical trials (by an average of 55 days).
- Increases patient access to precision medicine trials and novel therapies.
- Integrates with Electronic Health Records (EHR) for seamless clinical workflow.
- Offers both patient-centric and trial-centric matching modes.
- MatchMiner-AI can process unstructured clinical notes and match to a broader range of trials using LLMs.
Limitations
- Original MatchMiner primarily focused on genomic eligibility, potentially missing other crucial trial criteria.
- Matches are preliminary and require follow-up to ensure all trial criteria are met.
- Initial versions faced challenges with full integration into existing clinical workflows outside of specific research platforms.
- Some critical eligibility criteria, like prior lines of therapy or disease burden, were not captured by the original platform if only in free-text notes.
Based on reviews from: Dana-Farber Cancer Institute, Healio, npj Precision Oncology (via PubMed, PMC, and others), bioRxiv, AACR Journals, Innovations (Dana-Farber Cancer Institute), ResearchGate, YouTube (VJOncology)
Last updated: 2026-06-19
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Videos
Product demos, reviews, and walkthroughs for MatchMiner.
Using MatchMiner to improve clinical trial enrolment
VJOncology
MatchMiner: A new method of matching patients to precision medicine trials
VJOncology
AACR23: Harry Klein, PhD | Dana-Farber Cancer Institute
Dana-Farber Cancer Institute
Harnessing AI and Genomics in Clinical Trial Enrollment
JAMA Network
Pembrolizumab: defining standards of care in urothelial cancer
VJOncology
