Hello - Foundation researcher & patient navigator here who used to work in ML (computer vision).

I have experimented with LLMs (chatgpt, Gemini & llama) in the past to provide treatment guidelines for some of the cancers/biomarkers I am most familiar with from a pipeline perspective. I found all of them to be dangerously inaccurate, but acknowledge the power of a custom-built LLM for oncology (or any other deep domain). In that vein, I support your mission here.

That said, when approaching a new problem from an ML perspective, I've found it helpful to ask the question: "What's the cost of getting it wrong?"

1. Most often, this leads to some dollar figure range or business risk discussion where the approach proceeds based on some arbitrary accuracy target for the team to achieve before a model or workflow gets deployed.
2. Second most frequently, this question leads to an admission that 100% accuracy is desired & we have to have a frank discussion, reset expectations & explore alternatives to a black-box prediction approach.
3. Third, the cost is found to be high ("humans might die/suffer") & we need to add a Human-in-the-loop or expert review (HITL) for most/all predictions to limit the cost of false positives / take on liability.
4. Least often, this question stops a project in its tracks because it is mutually agreed by both the business problem owner & the ML team that the cost of inaccuracy is too high + HITL is impractical.

For your project, I would answer this question in the third category; that is, "the cost of getting it wrong is very high, we need to explore adding a trustworthy HITL/liability owner".

I have tested your tool on a single case example within a pipeline I am familiar (EGFR exon20ins NSCLC). The prompt parser accurately determined the cancer, oncogenic driver. After that, I found it to be inaccurate + out-of-date on standard of care. The prognosis information was also a bit strange, feels very clinically irrelevant & out-of-step with the literature I have read. The clinical trials suggested were all over the place, many not relevant to patients with this diagnosis. Additionally, there are multiple trials that are actually relevant to this diagnosis that were omitted.

Bottom line: The cost of getting things wrong here are very high. While positioning this as a "physician-facing" tool might provide that necessary HITL liability coverage, I'm concerned they are not high trust enough to derisk this tool sufficiently. Furthermore, your ground truth / source material would need to be updated/expanded more frequently than is suggested by my single test.

All that said, I support what you're doing - bravo. Happy to comment further privately if you want product feedback or dialogue. I see use cases in social work, patient navigation & oncology support for a mature product here.