Not loud percentages, but verifiable material: an external benchmark on state-exam questions, open comparisons with other AI, and a clear pilot procedure so a partner can verify on their own data.
The final clinical decision is always made by a doctor. Wizey works as an assistant: it speeds up review and reduces routine, but does not replace a specialist.
A blind test on 675 questions from an official state final-certification collection — with no training on this dataset and a strict Exact Match metric. This is not a promise of clinical infallibility: it is a check of the model's ability to work consistently with medical wording.
We tested material from the final certification in the specialty General Medicine (31.05.01). The algorithm did not see this dataset during training, and partially correct answers were counted as wrong.
Breakdown of the 675 exam tasks by block. Per-specialty accuracy is reviewed during the pilot on your scenario.
The benchmark confirms the model works confidently with medical terminology and logic. But for a B2B rollout this is only a starting point — final suitability is best verified on your scenario and an agreed set of examples.
Honest breakdowns on real clinical panels: where general models hallucinate, confuse units of measurement, or give potentially unsafe advice — and where Wizey differs through specialization, expert review, and medical sources. Each comparison is evergreen and built for independent verification.
A general LLM versus a purpose-built medical assistant: where ChatGPT drifts into generic phrasing, where it invents reference ranges, and which lab-analysis tasks belong only in a specialized service.
Read the comparisonClaude hallucinates less and more readily declines medical questions. Is that enough for interpreting lab results? Its strengths and clear limits, side by side with a specialized tool.
Read the comparisonGemini can process photos and PDFs. We look at whether multimodality helps when interpreting lab results, and where specialized OCR plus medical context beats a general multimodal model.
Read the comparisonWant the same breakdown for your task? Send 3–5 anonymized cases — during the pilot we'll compare Wizey with the model you use today. See use cases, integration, and the data perimeter.
Four recurring practices the product relies on. This is not marketing but a working process: sources, review, audit, and learning from mistakes.
Recognized clinical guidelines, current nosological protocols, up-to-date reference ranges for specific labs, and topical handbooks. We do not substitute guidelines with the model's generic answers.
The medical correctness of wording is checked by Wizey's Chief Medical Officer (Internal Medicine) and medical team. Experts review phrasing, disputed interpretations, and edge cases, and correct answer templates.
Continuous checking: a sampled audit of answers on typical cases, and a regular review of templates when new guidelines appear or partner-lab reference ranges change.
A spotted error is logged, reviewed by an expert, and turned into a template fix or a regression case. This keeps the model from repeating the same mistake on new analyses.
A pilot is meant to assess not an abstract model but concrete value inside a clinic, laboratory, or digital product. Three predictable steps and a clear result.
We check performance on text medical data resembling the partner's real flow: lab results, conclusions, discharge summaries. Data transfer goes through a secure perimeter.
Step 1 · InputThe partner's team checks the clarity, completeness, structure, and fitness of the result for the chosen role — lab tech, doctor, patient. You can use your own evaluation criteria.
Step 2 · ReviewAfter the pilot we record what to change in answer format, roles, and templates. Next — integration via API and terms under your plan.
Step 3 · NextMetrics differ across B2B scenarios. Labs look at add-on uptake and the quality of explanations to patients; clinics at how fast summaries are prepared and how much routine the doctor sheds; digital products at conversion into use and repeat visits. We agree on metrics before launch, so each side knows how to read the result.
Describe the process, the document types, and the user role. We'll propose a pilot-validation format, a set of materials, and evaluation criteria for your team.