Medidata
Indexed for the AI capabilities layered across the Medidata clinical trial platform rather than for the platform itself, which is treated as context under this index's product scoping rule. A Dassault Systemes brand, the underlying platform spans more than 38,000 trials and 12 million patients across roughly 2,300 customers and over one million registered users, anchored by Rave EDC. The AI products indexed here: Clinical Data Studio, an AI data quality management workspace that integrates Medidata and non Medidata sources, identifies data issues and safety signals, and is reported by one named customer to deliver up to 80 percent faster data review; Medidata AI Study Build within Designer, which automates study construction; and an AI imaging capability introduced at ASCO 2026 using proprietary algorithms including automated text detection that the company reports makes protected health information redaction 32 percent faster. Health Record Connect uses FHIR and health information exchanges to pull patient health records into trial data capture, reducing manual re entry at sites. The company reports its AI has supported more than 500 clinical studies over a decade, with more than 120 AI supported studies starting in 2025. Named customers for the AI products include Eisai.
Capability Axes
The third case of this grade in the index alongside Elation and Waystar, and descriptive rather than critical. What a sponsor buys is the clinical trial platform, anchored by Rave EDC across more than 38,000 trials; the AI capabilities are woven into that platform and accelerate work inside it. The company's own framing, that it weaves intelligence into more solutions across a unified platform, correctly places the AI as a layer. No sponsor selects Medidata for the AI alone, which is precisely the comparison a buyer needs against a specialist such as Saama or Deep 6.
Deployment scale is exceptional and long standing, with the AI reported across more than 500 clinical studies over a decade and more than 120 AI supported studies starting in 2025, against a platform footprint of 38,000 trials and 12 million patients. Named customer Eisai reports up to 80 percent faster data review with Clinical Data Studio, and the imaging capability reports 32 percent faster PHI redaction. Held back from A because the figures are customer or vendor stated without published methodology, and the platform's scale should not be read as evidence for the newer AI capabilities specifically, which is the same distinction applied to Medable.
Health Record Connect is the strongest element here and solves a real problem: it uses FHIR and health information exchanges to pull existing patient health records directly into trial data capture, so sites complete Rave EDC forms from the chart rather than re keying. Clinical Data Studio separately integrates non Medidata sources including third party labs and other electronic data capture systems, which matters because sponsors rarely run a single vendor stack.
No public pricing. Contact the vendor. Enterprise agreements with sponsors, typically scoped by study count and module. As with other platform vendors indexed under the scope rule, buyers should establish whether AI capabilities carry incremental cost or are included in platform licensing, since that determines the comparison against a separately priced specialist.
Clearly bounded to sponsor side clinical development for biopharmaceutical and medtech customers, spanning study build, data capture, data quality management, imaging, and patient facing capture. No claims outside clinical research.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
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Enterprise sponsor agreements scoped by study count and module | — | — | Vendor Published |
No rate card published. Enterprise agreements with pharmaceutical and medtech sponsors, typically scoped by study count and module selection. The recurring question for platform vendors indexed under this index's product scoping rule applies here: establish whether the AI capabilities carry incremental cost or are bundled into platform licensing, since a bundled capability compares very differently against a specialist priced separately.