Triomics

Reading hundreds of pages of narrative oncology records and reasoning against trial eligibility criteria is the product, and there is no non AI version of it. The founding observation is precise and worth repeating: structured data, roughly 20 percent of the record, was already tractable with conventional software; the value is in the other 80 percent sitting in free text.

The design decision that earns this grade is citation. Every match is traced back to the pathology, biomarker, and note level evidence that produced it, so a research coordinator verifies the reasoning rather than trusting a ranked list, and the visit preparation output is described as verifiable patient summaries. Output is a screened, ranked candidate set for human confirmation, not an enrollment decision. In trial matching, where a false negative means a patient never learns an option existed and a false positive wastes scarce coordinator time, showing the evidence is the control that matters.

Among the most specific architecture disclosures in this index. Rather than referring to a proprietary model, the company states OncoLLM is a constellation of eight specialized models ranging from 3 billion to 72 billion parameters operating agentically at the patient level. Naming the count and the parameter range lets a technical buyer reason about where inference runs, what it costs, and why smaller models handle narrow extraction while larger ones handle synthesis. Peer reviewed validation in Nature Digital Medicine, rather than a vendor whitepaper, substantiates the approach.

The strongest evidence position in the clinical trials category. Peer reviewed validation in Nature Digital Medicine with ASCO presentation, an independent pilot study at the Medical College of Wisconsin Cancer Center spanning four disease teams, and named deployment at Mount Sinai Tisch Cancer Center as the first NCI designated Comprehensive Cancer Center in New York City to run systemwide matching. Reported outcomes are specific and multi dimensional rather than one headline: 40 percent more trial matches, over 30 percent more enrollments, 67 percent less chart review time, and 100 percent screening coverage at three times manual throughput at one center. Deployment across top 10 US cancer centers is claimed. Buyers should still confirm the baseline each percentage is measured against.

No public pricing. Contact the vendor. Enterprise agreements with cancer centers, with a separate life sciences partnership line. Note the strategic investor: Precision Health Informatics, a wholly owned subsidiary of Texas Oncology, participated in the Series B, and a customer investing while extending its own use is a stronger signal than a logo.

Deliberately confined to oncology and tuned for it, which is the point rather than a limitation: oncology charts are unusually long and narrative heavy, trial criteria are unusually strict, and a general model performs worse on both. Workflow coverage has broadened sensibly along one dimension, from trial matching into visit preparation, registry abstraction, and real world data generation, all of which depend on the same underlying chart reading.