Medical Prompts: 30 Ready-to-Use Queries for Study and Practice

Medical AI prompts work best when they include four key components: role, context (course, subject, source), task, and output format. Below are 30 ready-to-use queries for studying and practice, grouped by task, along with rules to protect against hallucinated dosages and patient data leaks.

How to properly prompt an AI: The formula for a good prompt

A prompt is a text query submitted to an AI model. The difference between a weak and a strong prompt is more pronounced in medicine than in other fields: to a vague question, the model gives a vague answer, often without sources.

Compare two prompts. Weak: "tell me about CHF". Strong: "You are a professor of internal medicine. I am a 5th-year student preparing for my exam. Create a 7-point answer outline on the topic 'Chronic Heart Failure': definition, etiology, classifications, clinical presentation, diagnosis, treatment, prognosis. Base it on Russian clinical guidelines and note where you are uncertain". The second answer can be studied immediately, while the first will need to be redone.

Two more techniques: ask the model to quiz you (this way you actively learn rather than just read), and provide your own text instead of asking it to recall from memory. Your lecture notes are more reliable than the model's retelling "on the same topic".

Prompts for studying: master a topic, lecture notes, cheat sheet

Master a complex topic

• "You are a pathophysiology professor. Explain the pathogenesis of chronic heart failure to a 3rd-year student: first in 5 simple sentences, then point by point with medical terminology. Finally, provide 3 self-assessment questions."
• "Explain the Krebs cycle using an everyday analogy, and then list the reactions in order: enzyme, product, where NADH, FADH2, and GTP are formed. Present this as a table."
• "Compare type 1 and type 2 diabetes mellitus in a table: etiology, age of onset, body weight, insulin and C-peptide levels, autoantibodies, tendency toward ketoacidosis, and mainstay of treatment."
• "What are the most common mistakes and points of confusion for students regarding 'Cranial Nerves'? For each one, suggest a mnemonic to remember it correctly."
• "I have read the topic 'Community-Acquired Pneumonia'. Ask me 7 questions of increasing difficulty, one by one, waiting for my answer. After each answer, review what I missed."

"The last prompt is the most useful of all: it turns reading into verification. For oral practice, you can do the same thing using your voice; the platform features an oral exam where the examiner asks questions and provides a checklist-based review after the call."

Make a summary

• "Create study notes from this lecture text: sections with headings, definitions highlighted, a 'term and definition' table at the end, and 5 test questions with answers. [text]"
• "Condense these notes to a single page while preserving classifications, figures, and diagnostic criteria. Do not add any information of your own."
• "Turn these notes into a fill-in-the-blank text: remove key terms and numbers, number the blanks, and provide the answer key in a list at the end."
• "Make 15 'question and answer' flashcards from these notes for spaced repetition, with one fact per card."

"At WhiteCoat Pro, you don't have to compile notes manually with prompts: upload a lecture recording, a video via link from YouTube, VK Video, or Rutube, up to 40 photos of your notebook, PDF, Word, PowerPoint, or simply text. You'll get study notes structured like a textbook, complete with fill-in-the-blanks, tests, and spaced repetition flashcards. Learn more in the article about generating lecture notes with AI."

Generate study sheet

• "Create a cheat sheet on antibiotics: group, representatives, mechanism of action, spectrum, main side effects. Use a table, keep it as concise as possible."
• "Compile mnemonics to remember the 12 pairs of cranial nerves in order and by function: sensory, motor, mixed."
• "Create an emergency algorithmic cheat sheet for anaphylaxis management: step-by-step, with medications and routes of administration, without unnecessary text."

"You can generate a cheat sheet for your topic right on the platform without writing a prompt: simply name the topic or paste the text."

Exam preparation prompts

• "Here is a list of exam questions: [list]. Group them by topic and distribute them over 10 days so that the last 2 days are reserved for review."
• "Outline a 6-point answer plan for the exam ticket 'Hypertensive disease: classification, risk stratification, principles of management' to fit within a 5-minute limit."
• "Act as a strict pharmacology examiner. Ask me about beta-blockers, and then ask follow-up questions based on my answer. Do not give hints until I type 'review'."
• "Here is my answer to the exam ticket: [answer]. Grade it on a 10-point scale, and list what was omitted and where there are factual errors."
• "Create 10 multiple-choice questions on 'Acute Coronary Syndrome' with a single correct answer and a short explanation for each."

"The examiner role prompt works well in any chat, but the model will grade your answer based on its internal memory. If you want verification to be based on official study guides, use the AI tutor in 'Strict' mode. We discussed how to structure your daily preparation and avoid getting overwhelmed by exam tickets in the article on exam preparation with AI."

Prompts for clinicians: clinical cases and clinical guidelines

These queries are suitable for educational cases, clinical problem-solving tasks, and information retrieval. There is no real patient here: only gender, age, and clinical presentation, with no personally identifiable information.

• "Educational case: a 62-year-old female, dyspnea on ordinary exertion, lower extremity edema, history of myocardial infarction. Formulate a differential diagnosis and an investigative plan with justification for each item."
• "Analyze the educational clinical case according to the following structure: provisional diagnosis with justification, workup, differential diagnosis, management, and tactics. Case condition: [task text]"
• "What do the current clinical guidelines of the Russian Ministry of Health state regarding initial empiric antibiotic therapy for community-acquired pneumonia in an out-patient with no comorbidities? Indicate the section of the guidelines you are relying on."
• "How do the management approaches for HFrEF (heart failure with reduced ejection fraction) differ between Russian and European guidelines? If you are unsure about any point, state so explicitly."

Pay attention to the phrases «specify the section» and «if you are not sure, say so». They do not guarantee accuracy, but they make mistakes more noticeable. It is convenient to check the answer against the primary source: all 756 active clinical guidelines of the Russian Ministry of Health are open on the platform without registration, and the «Ask AI» button answers based on the text of the clinical guidelines and shows the sections it relies on.

If you are compiling a study case history, a separate prompt is not needed: the generator on the platform follows the department's schema, checks the diagnosis and treatment against clinical guidelines, and assigns an ICD-10 code. You can assemble it from your supervision notes (text, file, photo of handwritten notes, audio) or based solely on the disease, in which case the patient is simulated. Export to Word and PDF is available.

Prompts for a research article and presentation

Analyze a research article

• «Here is the abstract of the article: [text]. Analyze it according to the structure: research question, design, sample, primary endpoint, main results with numbers, limitations.»
• «Explain in simple terms what RR 0.78 (95% CI 0.65-0.93) and p = 0.006 mean in this article. Can the result be considered clinically significant, and what is missing for this?»
• «Help formulate a PubMed search query for the topic "SGLT2 inhibitors in HFpEF": keywords, MeSH terms, study type filters.»

Never ask a model to «find articles on a topic» without a search engine behind it: general chatbots tend to invent plausible links. It is more reliable to search in PubMed and feed the already found text to the model. The platform's «Research» section features PubMed search with statistics and a structured article breakdown.

Prepare a presentation

• «Create a 12-slide presentation outline on the topic "Type 2 Diabetes Mellitus: Modern Approaches to Treatment" for a 4th-year seminar. For each slide: title, 3-4 key points, and what to show (diagram, table, chart).»
• «Suggest 3 layout options for a slide on the pathogenesis of atherosclerosis that can be assembled from blocks and arrows.»
• «Shorten the text of this slide to 4 key points of no more than 10 words each, and move the rest to the speaker notes.»

You can assemble a ready-made presentation without prompts: the platform first generates a slide outline with diagrams and tables, which can be edited and rearranged, and then assembles it into layouts with illustrations and exports to PPTX and PDF. Only the outline consumes the limit; edits and assembly are free.

Prompts for Latin and medical terminology

• «Break down the term cholecystolithiasis into termino-elements, explain the meaning of each, and provide a translation.»
• «Translate the prescription into Latin in the correct form and explain the cases: "Take: Novocain solution 0.5% 200 ml. Dispense. Label: for infiltration anesthesia".»
• «Create 20 clinical terms on the topic "Diseases of the Stomach" with Latin spelling and translation, and then test me: name the Russian term, and I will write the Latin one.»

With Latin, models make mistakes in endings and cases more often than it seems, so always verify prescription forms with your Latin textbook.

What must never be entered into a neural network

The main rule for doctors and students in clinical practice: no personal data of the patient. Patient information constitutes medical confidentiality (Article 13 of Federal Law No. 323-FZ), and data by which a person can be identified is protected by the Personal Data Protection Law (Federal Law No. 152-FZ). Before pasting text into any neural network, remove:

• Full name, date of birth, address, phone number;
• Policy number, SNILS, medical history or outpatient chart number;
• Photos of documents and medical records containing these data;
• Rare details that make a person easily identifiable (workplace, unusual circumstances).

Instead of «Ivanov I. I., 12.03.1961», write «64-year-old male». For an educational task, this is sufficient. And remember that a neural network does not diagnose or prescribe treatment for a specific individual: it is a tool for studying and information retrieval, and the final decision rests with the doctor.

Why a good prompt does not guarantee an accurate answer

A prompt controls the form of the answer, not the model's knowledge. A general chatbot answers based on what it memorized during training: it may not know recent guidelines, may mix up Russian and foreign approaches, or may confidently state an incorrect dose. The request «cite your source» helps, but a model without a document database can invent a source as well.

Therefore, for medical facts, the formulation is less important than what the answer is based on. The Neural Network for Medical Professionals by WhiteCoat Pro answers using the platform's study guides (31 subjects, 3,400+ video lessons) and clinical guidelines, adds footnotes [1], [2] to textbook pages, and shows which video review to watch below the answer. You do not need to specify the role and sources in your request; the task and format are enough. It understands photos, PDF, and Word, works in Russia without a VPN, and provides up to 30 messages per hour on any account.

If you are used to ChatGPT or Claude, WhiteCoat Pro can be integrated with them via connector so you can search through guidelines and clinical protocols right in their chat. A comparison of 18 models for medical tasks is featured in the review of neural networks for doctors.

Frequently asked questions

What is a neural network prompt?

This is the query text: what you are asking to do, in what format, and what to rely on. The more precisely the role, context, task, and format are described, the more useful the answer will be.

How to properly write a medical prompt for a neural network?

Specify your role, your academic level (year of study, specialty), the specific task, the result format, and ask it to rely on Russian clinical guidelines with section references. Add «if you are not sure, write about it».

Do these prompts work in ChatGPT, GigaChat, and YandexGPT?

Yes, the formula is universal. The difference lies in where the model gets its facts: general models answer from memory, so medical details must be double-checked. Read more in the articles about ChatGPT for medical professionals and about GigaChat and YandexGPT for medicine.

Is it allowed to upload a patient's lab tests and medical records to a neural network?

Only fully anonymized ones: without full name, date of birth, address, policy number, and other data by which a person can be identified. Otherwise, this is a violation of medical confidentiality and personal data laws.

Why does a neural network hallucinate sources?

A general model generates plausible text, and a link for it is just text as well. If there is no document database behind the model, it can fabricate a nonexistent article. Choose tools that show the real source and open it.

Do I need to write prompts in English?

For studying according to Russian curricula, no: write in Russian, as this will align the answer closer to the terminology of your textbooks and Russian guidelines. English is appropriate for searching in PubMed.