AI-assisted clinical tools introduce a specific category of risk that doesn’t exist with textbooks or colleagues. We’re building DiffyD with those risks in mind, and we think you should understand them before relying on this in practice.
Non-determinismSubmit the same case twice and DiffyD may generate different challenge questions. This isn't a malfunction — it's how large language models work, producing responses from a probability distribution rather than a fixed lookup. Treat every output as one perspective from a well-read but fallible colleague, not a reproducible investigation result.
BiasThe model learns from medical literature, which reflects the populations, presentations, and clinical priorities that have historically received the most research attention. Atypical presentations in underrepresented groups, rare conditions, and non-Western clinical contexts may be handled less reliably. Apply additional scrutiny when the case doesn't fit the standard mould.
How we're building thisDiffyD is in active development. We evaluate outputs against real clinical cases, refine our prompts when questions miss the mark, and are building structured evaluation frameworks before expanding into higher-stakes settings. Your feedback — especially when DiffyD gets something wrong — directly shapes the product.