In April 2026, American physicians consulted a single AI service in approximately 27 million clinical cases. This is OpenEvidence, an evidence-based medicine search engine that, according to the company's data, is used by about 65% of US doctors. In Russia, most colleagues bookmark a regular chatbot instead, and a discharge summary containing the patient's name is sent right into the prompt.
The difference between these two situations is not the quality of the models. It lies in three questions you should ask any neural network before putting it to work: where it got the answer, what happens to the patient's data, and who is responsible if it makes a mistake.
Below is an analysis of eighteen models based on these criteria. No ads: some of them are useless in the clinic, and that is covered as well.
- What fits which task — quick table
- Specialized Medical AIs
- General-Purpose Models: GPT, Claude, Gemini
- Russian Models and Accessibility
- The Rest: Brief Overview
- Why 96% on USMLE means nothing
- Medical Confidentiality: What must not be pasted into a prompt
- Who is responsible for an AI error
- How to formulate a clinical query
- FAQ
What fits which task
There is no universal answer to the question "which neural network is best for a doctor" — just as there is no single best medication. There is a task and a tool suited for it.
| Task | What fits | Main limitation |
|---|---|---|
| Find evidence base with links | OpenEvidence, Perplexity | Check the primary source — the link may be irrelevant |
| Analyze a complex clinical case | GPT-5, Claude, Gemini (reasoning mode) | De-identified data only |
| Draft of a document, discharge summary, letter | Any top model | Proofread; the model makes up details |
| Data processing within the Russian perimeter | GigaChat, YandexGPT | Weaker on English-language literature |
| Translation and analysis of an English-language article | Claude, GPT-5, DeepSeek | Verify terms with a dictionary |
| Exam and accreditation preparation | WhiteCoat Pro AI | Tailored to the medical school curriculum, not the clinic |
| Diagnosing a patient | None of the above | Legally and clinically unacceptable |
Specialized Medical AIs
WhiteCoat Pro Neural Network
All other models on this list answer "medicine in general." Our AI tutor answers in context: it sees what year you are in, which topics are already covered, where you failed a test a week ago — and explains precisely the point that didn't click.
Underneath it is not abstract internet data, but its own database: 38 medical school subjects, 993 study guides, 136,557 pages of material, and 104,384 questions with explanations. Ask about kidney syntopy — get an answer from the exact study guide you will be tested on tomorrow, not a paraphrase of an English-language textbook.
What this provides in practice:
- explanation of any topic in the language of your department, rather than machine translation;
- analysis of test mistakes — not just "correct answer B," but why specifically B;
- state exam simulator with AI evaluation of open-ended answers;
- spaced repetition: the system automatically brings back what you started forgetting;
- clinical cases and 30+ calculators — CURB-65, qSOFA, CHA2DS2-VASc, and others.
The platform is used by over 30,000 medical students from 204 universities, who have collectively spent over 63,000 hours studying. You can start without registration — open a demo lesson.
This is our own product, and we state that plainly. The tutor is created for learning and exam preparation — prescriptions and bedside decision-making remain with the physician.
OpenEvidence
Answers not "from memory," but with quotes from peer-reviewed sources with direct links. Through a partnership agreement, full texts of the New England Journal of Medicine are available inside — including tables and figures, not just abstracts.
By mid-2026, the service had processed over 200 million clinical queries. Registration requires medical status verification and targets US clinicians — there is no direct access in Russia.
Important caveat: doctors interviewed by NBC News noted that the service makes errors and exaggerates in rare and complex cases. A separate complaint comes from instructors: residents stop distinguishing the essential from the trivial when the answer comes pre-packaged.
General-Purpose Models: GPT, Claude, Gemini
These are general-purpose models, and they are the ones most frequently in a doctor's hands. Their strengths are language, structuring, and parsing long texts. Their common weakness: they don't know what they don't know, and confidently fill in the gaps.
ChatGPT (GPT-5)
According to published evaluations, it shows the best results among general-purpose models on medical tests. In work, it is useful where disparate data needs to be connected: laying out a differential diagnosis, explaining a mechanism, rewriting text for an audience.
Limitation: access from Russia and an absolute ban on uploading unanonymized patient data.
Claude
Maintains the context of a long article or textbook chapter as a whole, less frequently "hallucinates" facts during summarization, and is noticeably more cautious with wording where data is insufficient. Convenient for analyzing English-language literature and preparing materials.
Gemini
Effectively parses diagrams, tables, and scans within a document, integrated with search. Google has a separate research line, Med-Gemini, tailored for clinical reasoning, but this is not the product available to ordinary users in a browser.
Russian models and the accessibility issue.
Here begins the practical side that English-language reviews ignore. For a physician working in a Russian clinic, the question of "which model is smarter" is secondary to the question of "where the data physically goes."
GigaChat (Sber) and YandexGPT
Both models are accessible without workarounds, work with the Russian language and medical terminology, and store data within the country. When analyzing English-language studies and performing complex clinical reasoning, they lag behind top foreign models—this is visible to the naked eye.
Yet they are the only option worth discussing at all when dealing with working data. And even here, anonymization remains mandatory: a patient's consent to data processing by the clinic does not equal consent to transfer it to a third-party AI service.
The rest: in brief
- Perplexity — search with mandatory citations. Good as a "fast Google", but you control the quality of the sources.
- DeepSeek — a strong and inexpensive model with decent reasoning. Data leaves the Russian Federation.
- Grok — fast, loose with wording. A questionable choice for medicine.
- Qwen, Kimi, MiniMax — Chinese models, some with open weights. Interesting for those deploying AI locally on their own servers.
- Llama, Mistral, Command A — open models. Their value for medicine lies in one thing: they can be hosted within the clinic, so data never leaves the perimeter.
- Copilot — essentially a wrapper around OpenAI models inside office software. Useful for documents, not for clinical practice.
- Reka — a niche multimodal model, rarely encountered in medical practice.
Why 96% on USMLE means nothing
The main argument of promotional roundups is scores on medical tests. According to published 2026 evaluations, top models score 91–96% on MedQA (the USMLE question bank). The numbers are impressive. Only they measure what a physician doesn't need.
MedQA is a multiple-choice test. The correct option is physically present on the screen, and the model's task is to choose it. A real patient encounter works differently: there are no options, some data is missing, and half the information comes from the physical examination rather than text.
The practical conclusion follows: a model's high exam score indicates that it passes exams well. Exactly that, and nothing more.
Medical confidentiality: what must never be put into a prompt
Information regarding the very fact of seeking medical care, the diagnosis, and the patient's condition constitutes medical confidentiality. This is a subcategory of personal data protected by Federal Law No. 152-FZ, and disclosing it without the patient's written consent is prohibited. Sending text to a foreign service also constitutes cross-border data transfer.
A separate pitfall is rare details. "A 34-year-old female dentist from a district hospital, 28 weeks pregnant, rare syndrome" — formally, there is not a single identifier, but in reality, colleagues will recognize the person from the very first line. De-identification is not about removing full names, but about making it impossible to identify a specific person.
A working format looks like this: "Woman aged 30–35, 3rd trimester of pregnancy. Complaints: … History: … Objective status: … Laboratory findings: …". Clinically, everything is preserved, while the person is unrecognizable.
Who is responsible for an AI error
Short answer: the physician. Detailed answer: also the physician, but with an explanation of why it cannot be otherwise.
Under Russian law, artificial intelligence is not a subject of medical practice. It has neither a license, nor duties, nor the capacity to bear liability. According to Article 70 of Federal Law No. 323-FZ, the attending physician organizes the examination and establishes the diagnosis — the decision remains with them. This position was publicly confirmed by legal experts at the St. Petersburg International Legal Forum in June 2026.
How to formulate a clinical prompt
The quality of the response is determined by the prompt more than by the choice of the model. A working structure consists of four parts.
- Role and context. «You are consulting an outpatient general practitioner. Answer according to clinical guidelines, indicate the level of evidence».
- De-identified case data in blocks. Age range, sex, complaints, history, objective findings, test results. Structured rather than in continuous text.
- Specific task. Not «what is wrong with the patient», but «list the differential diagnosis with justification and indicate which tests differentiate these hypotheses».
- Fact-checking. «Specifically note what in your response is not supported by sources and where you extrapolated». This item is most frequently omitted, yet it catches the majority of hallucinations.
And one more rule that saves the most time: prompt the model in the same language in which the required sources are written. Asking about a rare syndrome in English will yield a noticeably more accurate response than asking the same question in Russian.
Ask the neural network that knows your textbook
38 subjects, 136,557 pages of material, 104,384 questions with detailed explanations. Already 30,000 students from 204 universities. First lesson available without registration.
Open demo lessonFrequently asked questions
Which AI model is best suited for a physician in 2026?
There is no single best model; the choice depends on the task. For evidence-based search with citations, OpenEvidence is designed. For analyzing complex cases and drafting, GPT-5, Claude, and Gemini are suitable. For working within the Russian regulatory framework — GigaChat and YandexGPT. For studying and exam preparation — the WhiteCoat Pro AI, as it answers according to a specific course curriculum and guidelines rather than "medicine in general". None of them can be used as a source for a final diagnosis.
Is it permissible to upload patient data to ChatGPT?
No. Diagnosis, condition, and the very fact of seeking medical care constitute medical confidentiality and personal data protected by Federal Law No. 152-FZ. Transferring it to a foreign service also implies cross-border data transfer. You can only work with a fully anonymized description from which a specific individual cannot be identified.
Who is responsible if a physician makes a mistake due to an AI prompt?
The physician. AI is not a subject of medical practice under Russian law. According to Article 70 of Federal Law No. 323-FZ, the diagnosis is established by the attending physician, and citing an AI does not relieve them of liability.
What is OpenEvidence and why is there so much buzz about it?
An evidence-based medical search engine with answers featuring citations to peer-reviewed publications, including full texts of the NEJM. According to the company, it is used by about 65% of US physicians, with approximately 27 million clinical queries in April 2026. Requires verification of medical status and is tailored to the US.
Can an AI be trusted to make a diagnosis?
No. High scores on MedQA and USMLE reflect the ability to choose a correct answer option rather than manage a patient. On real clinical descriptions, models diverge more significantly and make more errors in rare cases. The appropriate role is as a source of hypotheses that the physician verifies against primary sources.
Which AI models are accessible in Russia without a VPN?
GigaChat and YandexGPT operate without restrictions and store data domestically. Some foreign services are accessible via intermediaries, but stability and payment are not guaranteed, and patient data must not be entered into them under any circumstances.
Will artificial intelligence replace the doctor?
In the foreseeable future, no. AI takes over routine tasks: document drafting, literature search, and data structuring. However, legally it is not a subject of medical practice, and it makes mistakes precisely where the cost of error is highest—in rare and atypical cases. The composition of routine tasks within the profession is changing, not the profession itself.
Is patient consent required to discuss their case with AI?
If the case is completely anonymized and it is impossible to identify the person, it is no longer a matter of personal data. If identifiers or rare details allowing patient identification remain in the text, written consent is required, and the clinic's general data processing consent does not replace it.
Further reading
- Online ECG: Calculators and Interpretation Simulator
- Biochemistry: Topic-by-Topic Breakdown
- Blog: Clinical Case Reviews by Specialty
- Platform Subjects Catalog
Sources
- Data on OpenEvidence usage: company reports and NBC News publications, 2026.
- Model evaluations on MedQA/USMLE: summary publications of 2026; figures are approximate, and full independent evaluations of the latest models have not yet been published.
- Doctor's liability: Article 70 of Federal Law No. 323-FZ; position of experts at the St. Petersburg International Legal Forum, June 2026 (RAPSI).
- Medical confidentiality and personal data: Federal Law No. 152-FZ.
- Registration of AI medical devices: Roszdravnadzor register; data transfer procedure to the MIS, July 2025; draft law on AI regulation in healthcare, Ministry of Digital Development, March 2026.
This material is educational in nature, does not constitute medical advice, and does not replace clinical guidelines, drug prescribing information, and the decision of the attending physician. Service names are provided for comparison; the platform is not affiliated with their developers.