Last updated: October 4, 2026
Finding a clinical trial can be surprisingly difficult. Thousands of studies are active at any time, and each has detailed inclusion and exclusion criteria covering age, diagnosis, previous treatments, laboratory results, biomarkers, location, and more. Traditionally, finding eligible patients has meant researchers and trial teams reviewing medical records by hand.
AI-powered matching platforms try to speed this up. They can analyze structured medical data and, in some cases, unstructured information such as physician notes, pathology reports, laboratory results, and genomic data, to flag trials that may be relevant to a patient. But the platforms are not identical: some are built for hospitals and research organizations, others for patients searching directly.
How We Reviewed These Platforms
This is a desk-research comparison. We:
- Read each platform’s public description of what it does and who it serves.
- Looked for published research evaluating AI trial matching, and noted which results apply to which system.
- Separated commercial products from academic research systems.
- Treated automated matches as leads for review, not confirmed eligibility.
This is not a ranking. The platforms serve different users, so there is no single «best» choice.
AI Clinical Trial Matching Platforms at a Glance
| Platform | Primary focus | How AI is used | Typical users |
|---|---|---|---|
| Deep 6 AI / Tempus | Patient identification within healthcare systems | Analyzes structured and unstructured clinical data | Health systems, research teams, sponsors |
| Antidote | Patient-to-trial discovery and recruitment | Match engine connects patients with potentially relevant studies | Patients, sponsors, research organizations |
| Massive Bio | Oncology and hematology trial matching | AI-assisted matching combined with patient support | Cancer patients, providers, sponsors |
| TrialMatchAI | AI-powered research matching | Processes clinical records and physician notes | Researchers and clinical teams |
| TrialX | Trial discovery and recruitment technology | Automated matching and trial search tools | Patients, researchers, organizations |
Capabilities and availability change over time. Some systems are designed for research or institutional use rather than direct consumer access.
Platform-by-Platform Review
| Platform | Patients | Providers / health systems | Researchers | Sponsors |
|---|---|---|---|---|
| Deep 6 AI / Tempus | – | ● | ● | ● |
| Antidote | ● | – | ● | ● |
| Massive Bio | ● | ● | – | ● |
| TrialMatchAI | – | ● | ● | – |
| TrialX | ● | – | ● | – |
● = listed as a typical user in the table above. TrialMatchAI’s “providers” refers to clinical teams; TrialX is also listed for “organizations,” which is not shown here.
1. Deep 6 AI and Tempus
Deep 6 AI works with healthcare data rather than relying only on information patients enter manually. It uses natural-language processing and machine learning to search structured and unstructured electronic medical record (EMR) data and identify patients who may meet trial criteria. Deep 6 AI was acquired by Tempus in 2025, bringing its matching technology into Tempus’s broader precision-medicine ecosystem.
This suits hospitals and academic medical centers, since candidates can be found from information already in their records, including clinical notes that may not exist as a simple structured field. A potential match is still not confirmed eligibility: research staff must review the patient’s information against the protocol.
2. Antidote
Antidote works from both the patient and research sides. Its Match search engine helps patients find potentially relevant trials based on their condition and other information, and it aims to simplify complex trial criteria. Its recruitment services are aimed at sponsors and research organizations, so the platform sits between trial discovery and patient recruitment rather than acting only as a medical-record screening tool. See the Antidote website.
3. Massive Bio
Massive Bio focuses on oncology and hematology. It helps patients identify potentially relevant cancer trials, including studies outside their immediate healthcare organization, which matters because patients often do not know what is available beyond their local cancer center. It also combines technology with human support instead of treating an automated match as the final answer. Since eligibility can involve complicated medical information, human review helps decide whether a computer-generated match deserves further investigation.
4. TrialMatchAI
TrialMatchAI comes from academic research into automated patient-to-trial recommendation. A 2026 study in Nature Communications described it as an open-source, locally deployable system that processes both structured clinical data and unstructured physician notes, with a focus on transparency and explanations that can be reviewed at the criterion level. See the published paper.
Systems like this show how large language models could automate more trial screening. But an academic system should not be confused with a commercially deployed patient service: research performance, clinical deployment, regulation, data security, and workflow integration are separate questions.
5. TrialX
TrialX provides technology for trial discovery, recruitment, and research workflows. It shows that platforms can connect patients, researchers, and studies without acting as a fully autonomous eligibility system. This is useful because studies are spread across institutions and locations. For patients, the key question is whether the platform offers current studies that fit their condition and circumstances.
How Accurate Is AI Clinical Trial Matching?
A matching system can flag a trial that looks relevant without the patient being eligible. Protocols can contain dozens of criteria, and information may be missing, outdated, or hard for an algorithm to interpret.
| Study | System evaluated | Key finding |
|---|---|---|
| Nature Communications, 2026 | TrialMatchAI (end-to-end approach using heterogeneous clinical data) | Emphasized criterion-level explanations and reproducible benchmarking. |
| ESMO Real World Data and Digital Oncology, 2026 | AI system for oncology trial matching, 3,804 patients | F1 score of 0.82, and screening time cut from about 120 to about 30 minutes per patient in a prospective evaluation. |
Screening time per patient, manual vs. AI-assisted (oncology study)
Blue = automated step. Green = human step.
Benefits and Limitations
- Less time spent searching medical records
- Finds information buried in unstructured notes
- Helps patients discover studies they might not know about
- Screens large numbers of records quickly
- Records can be incomplete or outdated
- Criteria can be ambiguous and need medical judgment
- Bias if some populations are underrepresented in the data
- Sensitive health data raises privacy and consent issues
Speed should not be confused with accuracy. The ideal workflow is often automated screening combined with human clinical review. Privacy is critical too: matching can involve highly sensitive health information, so data security, consent, access controls, and regulatory compliance are essential.
Limitations of this article: we compared platforms from public information, not hands-on use. The field changes fast, and only two evaluations are discussed here, each covering a single system.
Can AI Find a Clinical Trial for Me?
AI platforms can make trial discovery easier, but an automated result does not confirm eligibility. A research team must review your medical information and the trial’s current criteria before enrollment. Trials also change quickly: a study listed online may have reached its recruitment target, moved, or updated its criteria.
- Talk to your healthcare professional about whether a clinical trial could be appropriate for you.
- Treat any AI match as a lead, not an answer.
- Contact the trial site or research team directly to confirm the study is still recruiting.
- Let the research team check eligibility against the current protocol.
Frequently Asked Questions
Can an AI platform confirm I am eligible for a trial?
No. A match is a starting point. The trial’s research team decides eligibility.
Are these platforms free for patients?
It varies, and some are built for hospitals or sponsors rather than patients. Check each platform’s current terms.
Is TrialMatchAI available to patients?
It is described as an open-source academic research system for researchers and clinical teams, not as a patient-facing service.
Is my health information safe on these platforms?
That depends on each platform’s privacy and security practices. Read the privacy policy and understand how your data is stored and shared before entering information.
Do AI matching tools work for every disease?
No. Some platforms focus on specific areas such as oncology, and performance differs across diseases and datasets.
Final Thoughts
Deep 6 AI/Tempus, Antidote, Massive Bio, TrialMatchAI, and TrialX approach the same problem in different ways: helping patients and researchers navigate a complex trial landscape. Some emphasize health-record analysis, others patient-facing discovery or cancer-specific recruitment. Human clinical oversight, privacy protections, and independent validation will remain important as the technology matures.
Related Reading on Helathora
Sources
- TrialMatchAI study, Nature Communications, 2026: nature.com/articles/s41467-026-70509-w
- AI oncology trial-matching evaluation (3,804 patients), ESMO Real World Data and Digital Oncology, 2026: sciencedirect.com/science/article/pii/S2949820126000251
- Antidote: antidote.me
- Tempus announcement of the Deep 6 AI acquisition.
- Massive Bio and TrialX: official product pages.