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In our previous lesson, we began exploring how AI can be applied in medical practice. Now we continue with the second part of this practical review and more:
Table of Contents
Shall we dive in?
Case 4: AI for summarizing medical studies
Do you really need to read an entire medical study? Sometimes yes, sometimes a summary is enough. AI tools can help transform scientific articles, clinical guidelines, or complex patient records into concise summaries. With this in hand, you decide how deep you want to go.
👉 Example: Imagine a doctor needing to summarize dense medical studies, focusing only on the main ideas and key findings.
✨ "Prompt: "Assume the role of a neurologist researching embolic stroke of undetermined source (ESUS). Write a summary of the provided text, following these instructions:
- Include only the main ideas and essential information; do not add external data.
- Accurately describe the study methods, including participants, interventions, and data collection.
- Clearly present key findings, indicating statistical significance.
- Use bullet points to organize the summary and highlight all technical terms and key concepts in bold."
[Paste text to summarize here]
Case 5: AI for preparing patient reports
Preparing patient reports takes time, right? AI tools can help draft parts of the report or simplify complex details.
👉 Example: Imagine you need to create a clear and precise report for your patients, summarizing treatment status and care so far.
⚠️ Important warning: do not include any personal patient identifiers (e.g., name, contact information, ID).
✨ "Prompt: Assume the role of a healthcare professional and, based on the patient’s medical history, generate a clear and understandable report for the patient. The report should include:
- A summary of care provided to date
- The patient’s response to treatment over time
- A clear, actionable plan for next steps
Structure the report in chronological order and use simple, easy-to-understand language suitable for direct patient communication. If any relevant information is missing, indicate to the doctor to provide it before continuing."
[Paste patient information here]
Case 6: AI for treatment planning support
Finally, there are AI tools to brainstorm treatment options.
👉 Example: After reviewing initial lab results, a doctor is unsure which treatment is best. They use an AI assistant to generate a range of options, expand diagnostic reasoning, and help define the best path forward. Use specialized tools and again: do not include any personal patient identifiers.
✨ Prompt: "Assume the role of a healthcare professional and help generate a comprehensive list of treatment options and management strategies for a 45-year-old female patient with the following findings:
- Symptoms: chronic fatigue, 7 kg weight gain in 6 months, mood changes (irritability, low concentration)
- Lab results: [include results]
- Diagnostic considerations: subclinical hypothyroidism, insulin resistance/prediabetes, vitamin D deficiency, rule out subtle Cushing’s syndrome or primary mood disorders
Consider:
- Pharmacological and non-pharmacological interventions for each suspected condition
- Lifestyle interventions addressing overlapping symptoms
- How to prioritize or combine treatments effectively
- Additional diagnostic steps to confirm or rule out conditions before finalizing the treatment plan"
Wait… but what if AI hallucinates? 🫨🫨🫨
Now that you have many examples of AI in practice, let’s address a hot topic: AI hallucinations.
An AI hallucination occurs when AI makes errors, mixes facts with fiction, or produces content that may be inaccurate or misaligned with reality.
This usually happens due to patterns in the training data. If the data is incomplete, biased, or unclear, the AI may invent information that sounds real but isn’t.
How to avoid AI hallucinations
#1 Use AI as a starting point
Use AI as a starting point for research, not the final authority. AI is a powerful support tool, but the final decision should always come from the doctor.
For example, when AI generates a patient summary, review it carefully to ensure it matches the consultation notes. You can also ask follow-up questions like: “Can you show which parts of the summary come directly from the patient notes?”
#2 Don’t confuse fluency with accuracy
Just because the text reads smoothly doesn’t mean it’s correct! AI can produce convincing but outdated or biased results. Verify any AI suggestions against clinical guidelines (e.g., WHO, CDC, or local authorities) and check for generalizations or stereotypes.
#3 Not all AI tools are equal
Generic AI platforms are not designed specifically for medicine. Always check how the tool stores and uses data, and prefer AI solutions with proven healthcare expertise.
#4 Watch out for copy-paste pitfalls
LLMs trained on internet content can inadvertently reproduce copyrighted material. Avoid copying AI text directly into official documents—rewrite in your own words and use plagiarism checkers when needed.
✨The game-changing demo: meet Noa Notes ✨
To wrap up our series, here’s the most exciting part: at the end of the video, you’ll see a demo of Noa, our AI assistant designed to simplify clinical documentation. Watch how it works from consultation to final structured summary.
Want to see it in real time? Request a demo and try it out with our team. You’ll see how medical notes are automatically generated in the patient record.
