The delay in translating trial data into usable outputs like reports and the shortage of biostatisticians and medical writers have significant financial and scientific consequences. AI can help reduce these delays and associated costs, though it may not solve all the problems, it can address some of them. Readout AI leverages its AI and pharmaceutical expertise to establish a strong competitive advantage. They emphasize the importance of data and distribution for defensibility in the evolving AI landscape, focusing on integrating their solutions with Contract Research Organizations (CROs). They are also working on a user-friendly application with the aim of creating a brand advantage within the scientific community, having already validated their approach against established studies.
What is the problem?
The time between having trial data and generating human consumable outputs (reports, abstracts, posters, etc.) is long and error prone (such as copy-paste errors). Partly there are too few biostatisticians and medical writers, and partly the work is unglamorous (as compared to cool, new statistical algorithms) and so it languishes. Yet, this time lag is not only significant but significantly impactful. Waiting for an interim analysis costs both real money as the trial runs, and has an opportunity cost of potentially closing down a failing trial earlier. The cost of lags in poster or abstract generation may be a missed scientific communication opportunity. And obviously, shaving weeks or months from the time it takes to publish trial data is crucial. All of these lags can be minimized when AI can perform the biostatistics and medical writing. They don't claim to solve all of these problems, but they can begin to.
What is their solution?
Readout AI's combination of AI expertise and pharmaceutical service expertise is a deep moat. They know how to successfully build and deploy AI products for the Life Science industry and they have contacts at both major pharmaceutical companies and Contract Research Organizations. It's their belief that AI is rapidly democratizing, and so defensibility can no longer be based solely on AI or models, but must be based on data or distribution. They are not a health system and so they don't have the data. Therefore, they are very focused on distribution, working with CROs to integrate their solution deeply into their workflows. On the pharmaceutical side, they are building an intuitive application with the hope that first mover and network effects eventually build a brand moat for their scientist focused product. They have already validated against known studies.
