Home › Blog › Gynaecology › AI Accuracy in Gynaecology

Quick Answer

Published studies from 2024-26 show a consistent split: AI chatbots perform well on general and patient-facing gynaecology questions but lose accuracy on guideline-specific clinical queries. A 2025 analysis of ChatGPT on cervical cancer questions found strong performance on frequently asked questions and measurably lower accuracy on ESGO/ESTRO/ESP guideline-based items (Int J Gynecol Obstet, 2025). Reference fabrication compounds the problem: 28.6 percent of citations generated by GPT-4 in one comparative analysis were hallucinated (Chelli et al, J Med Internet Res, 2024). For Indian OBGYNs, an additional failure mode sits on top of these: generic models default to ACOG and WHO framing and rarely surface FOGSI or NCG recommendations at all.

Key Takeaways

• GPT-4 class models now pass OBGYN specialty examinations that GPT-3.5 failed, including all three recent sessions of the Polish national specialty certification examination, where the older model failed the Autumn 2024 sitting (Sci Rep, 2025).
• The accuracy gradient is consistent across studies: high on general knowledge, lower on guideline-specific and complex multi-step questions. In cervical cancer, ChatGPT scored well on FAQs but underperformed on ESGO/ESTRO/ESP guideline items (Int J Gynecol Obstet, 2025).
• Reference reliability remains the weakest link: hallucinated citation rates of 39.6 percent for GPT-3.5, 28.6 percent for GPT-4, and 91.4 percent for Bard across 471 generated references (Chelli et al, JMIR, 2024).
• In a prospective double-blind evaluation of ChatGPT-5 on 15 obstetric and gynaecological emergency scenarios, responses were benchmarked against ACOG, RCOG, and WHO guidance, not against any Indian source (BMC Emerg Med, 2025). No published concordance study has yet tested a general chatbot against FOGSI GCPR or NCG recommendations.
• Retrieval grounding is the documented fix, not model scale alone: a 2025 JAMIA meta-analysis of 20 studies found retrieval-augmented generation significantly improved LLM accuracy over baseline (pooled OR 1.35; 95 percent CI 1.19-1.53).

Why This Question Matters at the Indian Point of Care

Obstetrics and gynaecology may be the specialty where the cost of a confident wrong answer is highest. Decisions about drug safety in pregnancy, screening intervals, and emergency obstetric management are made quickly, often in settings without a second opinion available. Indian clinicians are already using general chatbots for exactly these questions; the FOGSI membership of over 45,000 gynaecologists spans practice settings from tertiary academic centres to single-doctor clinics, and it is the second group that leans hardest on whatever answer engine is in their pocket.

The question is therefore not whether AI will be used in Indian OBGYN practice. It is already being used. The question is where the published evidence says these tools can be trusted, where they fail, and what the failure looks like from a Mumbai or Patna consulting room rather than a Boston one. That last distinction, the divergence between Western defaults and Indian practice, is the thread running through everything below.

What Does the Published Evidence Show on AI Gynecology Accuracy?

The 2024-26 literature gives a more granular picture than the early GPT-3.5 era studies, and three patterns recur.

Pattern 1: Newer models pass examinations older models failed. A 2025 study in Scientific Reports tested ChatGPT-3.5 and ChatGPT-4 on more than 350 validated questions from three recent sessions of the Polish Specialty Certification Examination in Obstetrics and Gynecology. GPT-4 passed every sitting; GPT-3.5 failed the Autumn 2024 examination. The authors flagged two boundaries that matter clinically: accuracy dropped on complex questions requiring multi-step reasoning, and performance was significantly worse in Polish than in English. The language finding should register with Indian clinicians who query in Hindi, Tamil, Bengali, or mixed-language phrasing.

Pattern 2: The general-versus-guideline split. A 2025 study in the International Journal of Gynecology and Obstetrics assessed ChatGPT on cervical cancer questions drawn from two pools: frequently asked questions and items built from ESGO/ESTRO/ESP guidelines. Performance was strong on the FAQ set and measurably weaker on the guideline set. The same split appears outside gynaecology: a 2025 prospective study of ChatGPT-4 on urothelial carcinoma questions found 100 percent accuracy on patient-focused FAQs against 90 percent on guideline-derived items. The direction is always the same. The closer a question sits to a specific, versioned, dated recommendation, the less reliable the answer.

Pattern 3: Emergency scenarios expose the ceiling. A prospective, double-blind, scenario-based study published in BMC Emergency Medicine in 2025 evaluated ChatGPT-5 on 15 obstetric and gynaecological emergency scenarios built from ACOG, RCOG, and WHO guidance. The study design itself is the finding that matters here: even for the newest model generation, evaluation is anchored entirely to Western and WHO references. Concordance with Indian national protocols for postpartum haemorrhage, eclampsia, or obstetric sepsis has simply never been measured.

Earlier work rounds out the picture. Li et al showed ChatGPT outscored human candidates on a virtual OSCE in obstetrics and gynaecology (Am J Obstet Gynecol, 2023) but underperformed humans in gynaecologic oncology, early pregnancy care, and surgical skills, the domains requiring layered reasoning. An expert evaluation of ChatGPT-4 in reproductive medicine found median accuracy of 3.5 and completeness of 3 on a 5-point scale (Int J Gynecol Obstet, 2025): usable, not dependable.

The reference problem sits underneath all of this. Chelli et al analysed 471 references generated by chatbots for systematic review tasks and found hallucination rates of 39.6 percent for GPT-3.5, 28.6 percent for GPT-4, and 91.4 percent for Bard (JMIR, 2024). A citation that does not exist cannot be checked, and an answer that cannot be checked is not evidence. Separately, a 2025 multi-model analysis in Communications Medicine showed leading LLMs are vulnerable to adversarial hallucination during clinical decision support: they can be induced to elaborate confidently on fabricated clinical details embedded in a prompt. The hallucination rates measured across chatbot models, and the practical tests that expose them, are examined in full in our earlier analysis.

Where Generic AI Fails Indian Gynaecologists Specifically

The concordance studies above share one blind spot: every comparator guideline is Western or WHO. For Indian practice, the divergence between those sources and Indian recommendations is not academic. Three examples define the gap.

Gestational diabetes screening. Ask a general chatbot how to screen for GDM and the default answer is the ACOG two-step approach (50 g glucose challenge, then 100 g OGTT). India’s national programme, following DIPSI methodology adopted in Government of India guidelines, uses a single-step 75 g oral glucose load administered irrespective of fasting status, with a 2-hour value of 140 mg/dL or above diagnostic. The Indian approach was designed deliberately for a health system where asking a rural antenatal patient to return fasting means losing her to follow-up. A chatbot answer that is perfectly concordant with ACOG is discordant with the protocol an Indian medical officer is audited against.

Cervical cancer screening. India carries roughly one fifth of the global cervical cancer burden. The FOGSI Good Clinical Practice Recommendations stratify screening by resource setting: HPV DNA testing where feasible, visual inspection with acetic acid (VIA) as a valid strategy in low-resource settings, with screen-and-treat pathways that have no ACOG equivalent. NCG resource-stratified guidance follows the same logic. A generic model returning United States intervals and colposcopy-referral thresholds gives an answer that is unusable in a district hospital running a VIA-based programme.

Vaccination and drug availability. CDSCO approved Cervavac, the indigenously developed quadrivalent HPV vaccine, in 2022, and HPV vaccination for girls aged 9-14 has been announced for the national immunisation agenda. Chatbots trained predominantly on Western data reliably discuss Gardasil 9 pricing in dollars and miss the vaccine an Indian clinician can actually prescribe. The same pattern holds for formulation availability across misoprostol, injectable contraceptives, and magnesium sulphate regimens in the Indian market.

Failure Modes at a Glance

What Should Change Before Clinicians Rely on These Tools?

The evidence points to architecture, not patience. Waiting for the next model generation improves examination scores but has not closed the guideline-specificity gap, the citation problem, or the jurisdiction problem. What has demonstrably closed those gaps is retrieval grounding: forcing the model to answer from a curated, current, versioned evidence corpus rather than from parametric memory. The six testable properties that separate a grounded evidence tool from a merely fluent one are the practical way to assess this.

The numbers are consistent. The 2025 JAMIA systematic review and meta-analysis across 20 studies found retrieval-augmented generation significantly improved accuracy over baseline LLMs (pooled OR 1.35; 95 percent CI 1.19-1.53). A hepatology study reported accuracy rising from 43 percent to 99 percent when guidelines were supplied as structured context (npj Digit Med framework study, 2024). A 2025 radiology consultation study found retrieval grounding eliminated hallucinations entirely in its controlled comparison, from 8 percent to zero.

This is the design logic behind OneRX, an AI clinical decision support platform for doctors: answers retrieved from current guidelines and primary literature, with citations that resolve, and with Indian sources (FOGSI, NCG, ICMR, CDSCO status) in the corpus alongside international ones. The platform synthesises and presents evidence; the clinical decision stays with the clinician. What that standard looks like applied property by property is set out in our companion piece on what evidence-based decision support should look like in OBGYN.

Practical Guidance: Where General Chatbots Are Defensible in OBGYN Today

The task matters more than the tool. Our task-level framework sets out where general chatbots are defensible today across clinical work generally; the OBGYN summary is below.

• Reasonable uses: drafting patient counselling language, structuring differential lists to review against a verified source, summarising a paper the clinician supplies, translating patient instructions.
• High-risk uses to avoid: screening intervals and protocols, drug dosing and safety in pregnancy, emergency management sequences, anything answered with a citation the clinician has not personally resolved, and any India-specific programme or regulatory question.
• The operating rule: treat a general chatbot as an articulate colleague with an unknown training date and no accountability. Verify anything that would change management.

Frequently Asked Questions

Is ChatGPT accurate for gynaecology guideline questions?

Partially. Studies from 2024-26 show strong performance on general questions and specialty examinations but reduced accuracy on guideline-specific items, complex reasoning, and non-English queries. On cervical cancer, accuracy dropped precisely on ESGO/ESTRO/ESP guideline-based questions (Int J Gynecol Obstet, 2025).

Has any AI chatbot been validated against FOGSI or NCG guidelines?

No published concordance study has benchmarked a general-purpose chatbot against FOGSI GCPR or NCG resource-stratified recommendations as of August 2026. All major OBGYN evaluations use ACOG, RCOG, WHO, or European society comparators.

Why do AI models fabricate references in gynaecology answers?

Because base LLMs generate statistically plausible text rather than retrieving documents. Chelli et al measured hallucinated citation rates of 28.6 percent for GPT-4 and 91.4 percent for Bard (JMIR, 2024). Retrieval-grounded systems answer from indexed sources, which is why their citations resolve.

Can AI be used safely for drug safety queries in pregnancy?

Not as a final authority. Class-level teratogenicity answers are often correct, but formulation availability, Indian brand equivalents, and recent label changes are unreliable. Verify against a current formulary or a retrieval-grounded evidence platform before prescribing.

Will newer models fix these problems on their own?

Examination performance improves with each generation, but the guideline-specificity gap, citation fabrication, and jurisdiction blindness persist across model generations. The documented fix is retrieval architecture with a curated, current, India-inclusive corpus, not scale alone.

Conclusion

The 2025-26 evidence on AI gynecology accuracy is neither alarmist nor reassuring. It is specific. General chatbots now pass OBGYN examinations, handle patient-facing questions competently, and remain unreliable exactly where clinical stakes concentrate: versioned guidelines, verifiable citations, and jurisdiction-correct recommendations. For Indian OBGYNs the gap is wider still, because no general model has been tested against, or trained to prioritise, the FOGSI, NCG, and national programme guidance that governs actual practice. Evidence platforms built on retrieval from current, India-inclusive sources address the documented failure modes. Until a tool shows its sources and its sources resolve, the burden of verification stays with the clinician.