Highlights:
- AI tools, especially large language models (LLMs), can speed up drafting a case report.
- They are not able to effectively spot logical flaws, unclear novelty or weak implications for practice.
- A human editor can improve flow, coherence, and technical word choice in an AI-generated manuscript.
- An optimum workflow for busy clinicians is to use AI to generate a first draft, verify the accuracy of the AI output, and then have the manuscript checked by a subject-matter expert editor who also ensures adherence to your target journal’s guidelines.
- Always upload deidentified data into an AI tool and get confirmation beforehand that it’s okay to use that tool for writing.
Introduction
Case reports are one of the easiest articles that a busy medical researcher can write, and also one of the hardest to get published. A good case report cannot just describe what happened to a patient. It has to give another clinician something genuinely new to learn from it.
Large language models (LLMs) are now very good at the mechanical parts of getting a case report onto the page: fixing grammar, formulating sentences, organizing the standard sections, and producing a clean first draft from rough notes. But they are not reliable for judging whether the case actually teaches anything, whether the clinical logic holds together, and whether the manuscript reads the way an experienced reviewer expects a case report to read. That gap is where a human editor, with experience in medical publishing, becomes very important.
See also: What is a Case Report? Format, Tips, Examples
This article explains what AI genuinely handles well when drafting a case report, where a human pass adds real value, how to avoid AI hallucinations in your case report, and the ethics of putting patient information into an AI tool in the first place.
What AI Is Good At
Modern AI academic writing assistants are reliable for the parts of writing that are essentially mechanical: converting a clinician’s rough, note-form account of a case into full sentences; ensuring no grammar or punctuation errors; fine-tuning tone and style; and ensuring consistent headings. AI is also useful for producing a first pass at abstracts.
Example: turning clinical notes into academic writing suitable for a journal
Raw input from a clinician might look like this:
“58F, 3 days chest pain, worse breathing, hx breast ca 2019 treated, came to ED, ECG normal, d-dimer high, CT showed PE bilateral, started on apixaban, discharged day 4.”
An LLM can turn this into: “A 58-year-old woman with a history of breast cancer treated in 2019 and in remission presented to the emergency department with a 3-day history of chest pain and worsening dyspnea. Her electrocardiogram was unremarkable, but D-dimer was elevated. Computed tomography pulmonary angiography demonstrated bilateral pulmonary emboli. She was started on apixaban and discharged on day 4.”
This is a genuine time saver. The sentence structure, tense, and flow are all correct, and nothing here required clinical judgment beyond faithfully transcribing what was said.
Where the AI Draft Falls Short
1. Flow across the whole manuscript, not just within paragraphs
AI drafts tend to read well sentence by sentence but can feel disjointed at the level of the whole report. A common failure is repeating the same clinical detail in both the case presentation and the discussion in near-identical wording, or introducing the teaching point of the case too late, after the reader has already lost interest.
A human editor reading start to finish can restructure so that the discussion builds on the case rather than re-describing it, and can move the sentence that states why the case matters into the first paragraph, where an editor and reader will actually look for it.
2. Logical inconsistencies the model won’t flag
An LLM will not usually notice that a stated timeline doesn’t add up or that a treatment decision doesn’t follow from the findings described.
Let’s take the example of a draft describing a patient at 32 weeks’ gestation who presents with reduced fetal movement: one paragraph states that amniotic fluid index was 16 cm (in the reference range), and then the next paragraph states that the patient was admitted for an emergency Cesarean section owing to oligohydramnios. Both sentences are individually well formed, so an AI tool is likely to let the contradiction pass and won’t point out that the stated indication for Cesarean delivery doesn’t match the finding reported earlier in the same case. A human reader with clinical knowledge is far more likely to notice the contradiction, because catching it requires understanding the whole clinical picture and not just checking each sentence for grammar.
3. Correct use of technical terms in context
LLMs frequently use medical terms fluently but not always precisely. For example, they may treat sensitivity and specificity as loosely interchangeable in a sentence about a screening test. Another frequent issue is loose staging or grading language, for example, describing a tumor as advanced without citing the actual stage, or using “mild” and “moderate” inconsistently across a report describing severity. A subject-matter expert reviewing the draft is the only reliable check for this kind of precision, because the error is invisible to a grammar check and often invisible to the model itself.
4. Whether novelty and teaching value are clearly presented
Journals accept case reports for a narrow set of reasons: an unexpected presentation, a diagnostic pitfall, an unusual complication, a rare drug interaction, or a management approach that goes against the usual pathway. An AI draft will dutifully describe the case but rarely makes the explicit argument for why it is worth publishing.
Example: sharpening the teaching point
The first sentence of the discussion of a case report generated by AI reads as follows:
“This case sheds light on the underlying causes of jaundice in neonates who present with prolonged severe hyperbilirubinemia.”
Comment [Editor]: This is sentence is grammatically correct, but it reads like a generic reminder to examine the causes of jaundice. Consider adding more details here to clarify what this case teaches other neonatologists.
Comment [Author]: Everyone assumed breastfeeding jaundice. We almost decided to not check bilirubin levels at the 3-week visit because he was exclusively breastfed and had gained weight. That’s something other clinicians should be aware of.
Comment [Editor]: Thank you for this clarification. Please check if the following revision captures what you were trying to convey:
Final edited version: “This case is notable because the infant’s jaundice persisted well beyond the 14-day cutoff for physiological jaundice, yet was initially managed as routine breastfeeding jaundice without further work-up as the infant was gaining weight appropriately. The eventual diagnosis of biliary atresia illustrates why neonatologists who observe prolonged severe jaundice past 14 days of life in an exclusively breastfed infant should refer the infant for conjugated bilirubin measurement.”
The final edited version says something specific and arguable. The AI-generated version is true of almost any neonatal jaundice case and would not teach a neonatologist anything that isn’t already covered in a textbook. (Note that the editor got information regarding the diagnosis and conjugated bilirubin measurement from elsewhere in the manuscript. The editor also verified that the previous sections had already mentioned that the infant was exclusively breastfed and that his weight gain had been documented.)
A Practical Workflow for Using AI to Write Case Reports
A workable approach is to let AI handle the first structural draft: converting notes into prose, applying standard case report formatting, and producing a readable abstract. The next step should be a careful check for accuracy, cross-verifying all data against the patient’s chart, diagnostic imaging, etc. Finally, run the manuscript by a professional medical editor who focuses specifically on four questions:
- Does the manuscript flow as a single argument rather than a list of facts?
- Is the clinical reasoning internally consistent from presentation through to discussion?
- Is every technical term used correctly and precisely?
- Is the reason this case deserves publication stated explicitly and early?
None of these four aspects can be checked by asking an AI tool to proofread or improve a draft.
What the Researcher Must Do Before Using AI to Draft
| Step | Action | Why It Matters |
| 1 | Obtain informed consent from the patient (or legal guardian) specifically for publication of their case, per journal/CARE guideline requirements | Standard ethical requirement for any case report, independent of AI use |
| 2 | De-identify all data (names, dates, MRNs, geographic identifiers, rare identifying details) before it touches any AI tool | Prevents personal health information exposure via a third-party system; required under HIPAA/GDPR-equivalent rules |
| 3 | Check institutional and journal policy on AI use: whether AI-assisted drafting is permitted, and what disclosure is required | Many journals (e.g., ICMJE-aligned) mandate disclosure of AI tool use in methods/acknowledgments sections; some restrict it entirely |
| 4 | Verify data-handling/privacy terms of the specific AI tool (e.g., is input used for model training? Is it enterprise/HIPAA-eligible?) | Consumer-facing AI tools often are not appropriate for even de-identified sensitive health data unless covered by a BAA or equivalent |
| 5 | Compile and organize the source clinical record first (labs, imaging reports, timeline, treatment course) into a clean document | Gives the AI accurate grounding material and reduces the temptation for it to “fill gaps” |
| 6 | Decide the reporting standard to follow (e.g., CARE guidelines) and have the checklist ready | Ensures the AI draft is structured correctly from the outset rather than needing rework |
| 7 | Define the AI’s role in writing (formatting/style help vs. content generation) and plan your fact-checking process | Keeps the researcher (not the AI tool) responsible for clinical accuracy and reasoning |
| 8 | Confirm authorship rules with co-authors and preferred journals (AI cannot be listed as an author under ICMJE criteria) | Avoids authorship disputes or journal rejection for policy violations |
A general note, since this sits close to real patient care: whatever tool is used, the researcher (not the AI tool) remains fully responsible for verifying every clinical fact, date, and value against the source record before submission. AI-assisted drafts should be treated as unverified until checked line-by-line.
Strong vs. Weak Prompts for AI-Drafted Case Reports
| Weak Prompt | Why It’s Weak | Strong Prompt | Why It’s Stronger | |
| 1 | “Write a case report about a patient with lupus nephritis.” | No patient specifics, no structure, invites fabrication of clinical details | “Using the attached de-identified clinical timeline (labs, biopsy report, treatment course), draft a case report following the CARE guidelines structure (Title, Abstract, Introduction, Case Presentation, Discussion, Conclusion). Do not add any clinical detail not present in the source material.” | Grounds the AI in real, supplied data; specifies a recognized reporting standard; explicitly forbids invention |
| 2 | “Make it sound more professional/academic.” | Vague stylistic request with no control over factual content | “Revise the tone to match journal X’s style guide (attached), keeping every clinical fact, date, and lab value exactly as in my draft. Flag any sentence where you had to infer or generalize instead of using stated data.” | Separates style editing from content generation; asks AI to self-report uncertain inferences |
| 3 | “Summarize the patient’s history for the case report.” | AI may compress/omit clinically significant negatives or timeline order | “Summarize only the events in this timeline (attached) in chronological order, preserving all dates, dosages, and lab values verbatim. Do not interpret significance or add causal language.” | Constrains to extraction/reformatting, not clinical interpretation |
| 4 | “What’s the likely diagnosis/discussion points for this case?” | Asks AI to perform clinical reasoning it isn’t licensed or reliable to do | “Given the discussion points I’ve already drafted (attached), check for internal consistency and suggest 2–3 relevant differential diagnoses to prompt my own literature review — do not state a diagnosis as fact.” | Keeps clinical judgment with the researcher; uses AI as a checklist/prompt generator, not decision-maker |
| 5 | “Find similar published cases and cite them.” | High risk of fabricated or mismatched citations | “List search terms and databases (PubMed, Embase) I should use to find comparable published cases. Do not generate citations yourself.” | Avoids AI hallucination of references entirely |
| 6 | “Fill in the patient’s demographics/history if I didn’t include them.” | Actively invites fabrication of PHI-adjacent or clinical facts | “Identify any sections where information is missing or ambiguous in my draft, and list them as open questions for me to fill in — do not generate placeholder content.” | Makes gaps visible instead of silently inventing data |
Here’s a new section you can slot in — likely after “A Practical Division of Labor” and before “The Ethics of Uploading Patient Data”:
How to Avoid Hallucinations When Using AI to Write a Case Report
Hallucination in a case report is particularly dangerous because it doesn’t look like an error — a fabricated lab value or a misremembered drug dose reads exactly as fluently as a real one. The following practices reduce the risk substantially.
Before drafting
- Give the AI tool the complete source material (notes, lab values, imaging reports) rather than asking it to “write up a case of biliary atresia” from general knowledge. Hallucination risk rises sharply whenever the model is filling gaps instead of transcribing.
- Avoid asking the AI to generate specific numeric values, dates, or reference ranges from memory. If a normal range or a drug dosage needs to appear in the text, supply it yourself or verify it against a primary source rather than trusting the model’s recall.
- Be wary of asking for citations. LLMs are well known for fabricating plausible-looking journal references (real author names attached to titles and page numbers that don’t exist). Any citation an AI tool produces should be treated as a placeholder to verify, never as a finished reference.
While drafting
- Read every clinical claim in the draft against the source notes line by line, not just for tone but for content. Check that every value, date, and finding traces back to something actually documented.
- Watch for the model “smoothing over” gaps in the notes. If the input said “labs pending” and the output says “labs were within normal limits,” that’s a fabricated result, not a paraphrase.
- Be alert to invented specificity, for example, an AI draft turning “recent surgery” into “surgery three weeks prior” or “mild anemia” into “hemoglobin of 9.8 g/dL” when no such figure was given. Precise-sounding numbers are exactly where hallucination hides best, because they read as more credible than vague language.
After drafting
- Run a dedicated fact-check pass separate from the language-and-flow edit. Treat checking claims against the source record as its own step, not something that happens automatically while reading for grammar.
- Verify every named drug, dose, guideline, and cited statistic independently, especially anything that wasn’t explicitly present in the original clinical notes.
- If co-authors or the treating team are available, have them read the final draft specifically for factual accuracy before submission. A second clinician who was present for the case is often the fastest way to catch a subtly wrong detail that reads perfectly plausibly.
A general rule of thumb: the more fluent and specific an AI-generated sentence sounds, the more it deserves scrutiny rather than less. Hallucinated content is rarely vague or hedged. It tends to be confident and detailed, and this is precisely what makes it easy to miss on a quick read.
The Ethics of Uploading Patient Data into AI Tools
Before you can run to ChatGPT to upload your case notes, there is a prior question: What patient information is safe to give an AI tool at all? Pasting an unredacted clinical note into a general-purpose chatbot can constitute an unauthorized disclosure of protected health information, even if the model provider says it doesn’t train on submitted data.
The safer approach is de-identification before anything is entered into an AI tool. You need to remove names, exact dates, medical record numbers, specific institution, and any other detail that could identify the patient, either alone or combined with other case details, before inputting anything into an AI tool.
Also, note that the usual patient consent form for publication doesn’t automatically cover uploading the patient’s records into a third-party AI system. It’s best to get a clear sign-off from your hospital’s data privacy team as well as IRB/research ethics office before entering any patient’s data into your own ChatGPT account or any other AI tool.
These issues are not so pressing with the use of human editors as long as you choose reputed professional editing services with strong privacy and security systems. For example, Editage has ISO/IEC 27001:2013–certified IT security systems and all editors sign a rigorous confidentiality and non-disclosure agreement before they are assigned any client’s manuscript or data.
Aligning the Case Report with Journal Requirements
Many authors don’t realize that there’s no single, universally accepted format for a “case report”. While the majority of journals expect an Introduction, Case Presentation, and Discussion, the exact section headings and requirements may differ. Let’s take a look at some examples:
Example 1: Blood
Blood doesn’t have a dedicated case report section, but will publish highly novel or important case reports as a Brief Report, Letter to Blood, or How I Treat article. Each type comes with its own structure and word limits that an author must adhere to.
Example 2: BMJ Case Reports
BMJ Case Reports publishes multiple types of case reports, not just the conventional ones. The journal asks authors to directly download and input text into their templates, which contain unique structural and content requirements. For example, the 2026 template for a Clinical Case Report has dedicated sections for Differential Diagnosis, Outcome and Follow-Up, and Patient’s Perspective, which require much more detail than what a typical case report provides.
Example 3: European Heart Journal – Case Reports
European Heart Journal Case Reports requires a special summary figure to be placed after the Introduction, and this figure should illustrate the key teaching points of the case for clinicians. The journal also has a Grand Round section for cases where multiple specialties were involved in diagnosis, evaluation, and treatment. Word limits, number of references, and even number of authors permitted differ for each.
The Role of a Medical Editor in Case Report Formatting
Currently, LLMs can generate a case report in conventional sections, but once you shortlist a target journal, a professional editor can help you ensure that the case report is properly aligned with the journal’s requirements.
How to Avoid Journal Rejection for Using AI
Journals aren’t rejecting manuscripts because the authors used AI, and in fact, many journals now expect it and have framed specific policies around AI use (e.g., JAMA). What gets manuscripts rejected or sent back is mishandling the disclosure, the authorship question, no actual underlying contribution to knowledge, or the underlying data. The following practices reduce that risk.
Disclosure
- Check the specific target journal’s instructions for authors before submitting. Policies differ in wording and placement (some want AI use noted in the methods, others in the cover letter or acknowledgments), and using the wrong format is a common, avoidable reason for desk rejection.
- State what the AI tool was used for, not just that one was used. “AI was used to assist with language editing of the draft” is a very different disclosure from “AI was used to generate the first draft from clinical notes.”
- Disclose every tool involved if more than one was used (for example, a drafting assistant and a separate grammar tool), rather than bundling them into a single generic statement.
- Don’t skip disclosure because the AI’s contribution feels minor. Editors increasingly screen for AI-generated text, and an undisclosed use that’s later detected is treated as a transparency issue, not a language issue.
Authorship
- Never list an AI tool as an author or co-author. Every major journal body has been explicit that AI cannot take responsibility for a manuscript’s accuracy or integrity, which is a requirement of authorship, so an AI byline is an automatic rejection or a demand to correct it before review proceeds.
- Make sure a human author has personally verified every clinical fact, reference, and figure in the manuscript, since responsibility for accuracy sits entirely with the named human authors regardless of what tool helped produce the sentence.
Content and originality
- A professional editor can make sure that the novelty and teaching value of the case are highlighted appropriately. Reviewers are quick to reject case reports that read as generic, and a disclosure statement won’t rescue a paper whose “why this case matters” argument is thin or reads like AI-boilerplate.
- Run the near-final draft through a plagiarism or text-similarity checker before submission. AI tools can reproduce phrasing that is closer to existing published case reports than authors expect, which some journals flag independently of AI disclosure.
- Avoid submitting a manuscript that reads as obviously AI-generated in tone throughout (repetitive sentence structures, generic hedging phrases, overly uniform paragraph lengths). Beyond the ethics of disclosure, this is now something reviewers notice and comment on unprompted, and it can color how carefully they scrutinize the rest of the submission. Professional editing is invaluable in cutting out wordy AI-boilerplate text and in flagging places where beautiful prose masks the fact that there’s actually nothing substantial said.
Data and consent
- Confirm that any patient data used to prompt an AI tool during drafting was properly de-identified beforehand, and be prepared to state this if asked. Your journal may request confirmation that no identifiable patient data was entered into a third-party AI system.
- Keep the patient consent-for-publication paperwork and any AI-use disclosure as separate items.
Bottom Line
AI is a genuinely useful first-pass writer for case reports: it is fast, it produces grammatically sound prose, and it can save a clinician hours of drafting time. But what actually gets a case report published is novelty and teaching value. LLMs cannot tell you whether the reasoning holds up, whether the terminology is precise, and whether the novelty and teaching value can convince a journal editor or peer reviewer that the case is worth publishing. Pairing an AI draft with your own check for accuracy and finally a human review from a subject-matter expert is the combination that is most likely to produce a case report that gets published.
Frequently Asked Questions
Do I need to disclose that I used AI (like ChatGPT) to write my case report?
Yes, in almost all cases. Most major journals now follow ICMJE or COPE-aligned policies requiring disclosure of AI tool use in the Methods, Acknowledgments, or a dedicated “AI Use” statement. Typically you need to name the tool, version, and what it was used for (e.g., “drafting,” “language editing”). Check your target journal’s specific author guidelines, as wording requirements vary.
Can ChatGPT or another AI be listed as a co-author on a case report?
No. ICMJE authorship criteria require accountability for the work, which AI tools cannot hold. Nearly all publishers (Elsevier, Springer, JAMA, NEJM, etc.) explicitly prohibit listing AI as an author, though it can be credited in acknowledgments or a disclosure statement.
Will journals reject a case report because it was drafted with AI?
Most journals accept AI-assisted drafting if properly disclosed and if the content is verified and substantially reviewed by human authors. Rejection risk comes from non-disclosure, fabricated citations or facts, or a paper that reads as generic/templated.
Is using AI to write a medical case report considered plagiarism?
Not inherently, but it can create plagiarism-adjacent risks: AI models may reproduce phrasing close to existing published text without attribution, or “self-plagiarize” boilerplate phrasing across many drafts. Running the final draft through a plagiarism checker (e.g., iThenticate, which many journals use themselves) before submission is standard practice.
How do I cite or reference an AI tool like ChatGPT in a case report?
Most style guides (e.g., AMA, APA 7th edition) now have specific formats for citing AI interactions. This could be in a footnote, as a personal communication, or an entry in the reference list that includes the prompt as well as a shareable link of the entire AI conversation. But note that AI output itself is considered a weak source, owing to the risk of hallucinations, and hence it’s much better to cite real, published journal articles, books, reports, posters, etc.
How much of a case report can be AI-generated before it’s a problem?
There’s no universal percentage threshold — the issue is verification, not volume. Journals generally distinguish between AI helping with language/structure/formatting (broadly acceptable with disclosure) versus AI generating clinical content, citations, or discussion points without human verification (high-risk and can lead to retraction if factual errors or fabricated references slip through).
Can AI-generated case reports contain fake or “hallucinated” citations?
Yes, this is one of the most common and serious risks. AI language models can generate plausible-looking but nonexistent references or misattribute real findings to the wrong study. Every citation in an AI-assisted draft must be manually verified against the original source (e.g., PubMed) before submission; never trust AI-generated reference lists as-is.
Do I need patient consent if I use AI to help write their case report?
Patient consent for publication is required regardless of whether AI is used, but there’s an added consideration: de-identified patient data should not be entered into non-HIPAA-compliant or public AI tools, as this could violate patient privacy protections even after consent is obtained for the eventual publication. Always get confirmation from your institute’s IRB before entering any patient data into an AI tool.
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