Beyond the AI Hysteria: Why ObGyn Physicians Should Embrace Large Language Models

An analysis of how LLMs can enhance obstetric care, why AI 'hallucinations' are less problematic than medical errors, and the critical prompt engineering skills every physician needs to master.

Based on a comprehensive survey of large language models (LLMs) in biomedicine (Artificial Intelligence In Medicine, 2025, ARTMED 103268), there are significant opportunities for obstetrics to benefit from these technologies, though successful implementation requires a nuanced understanding of both their capabilities and limitations.

Clinical Applications for Obstetrics

The research demonstrates that LLMs can achieve diagnostic accuracy comparable to human experts across medical specialties, which translates directly to obstetric care. These systems excel at integrating multiple data sources simultaneously, making them particularly valuable for pregnancy management where maternal labs, vital signs, imaging results, and fetal monitoring data must be synthesized quickly. In emergency situations like suspected preeclampsia or preterm labor, LLMs could assist with rapid risk stratification by processing complex clinical pictures that might otherwise require lengthy deliberation or specialist consultation.

The multimodal capabilities highlighted in the survey are especially relevant for obstetrics, where visual interpretation of ultrasounds and fetal heart rate tracings must be combined with clinical history and physical examination findings. Rather than replacing clinical judgment, these systems could serve as sophisticated second opinions, particularly valuable for providers in resource-limited settings or during overnight coverage when specialist expertise may not be immediately available.

Clinical decision support represents another significant opportunity, particularly for medication safety during pregnancy and lactation, where the evidence base is constantly evolving and drug interactions can be complex. LLMs trained on current obstetric literature could provide real-time guidance on evidence-based protocols while flagging potential contraindications or suggesting alternative approaches based on individual patient factors.

Reframing Error Rates and Realistic Expectations

The discourse around LLM "hallucinations" requires perspective when considered alongside documented medical error rates. While LLM hallucination rates in medical applications range from 0-24.6% according to this survey, physician diagnostic error rates consistently fall between 10-15%, with medical errors ranking among leading causes of death in healthcare systems. This isn't to diminish the importance of accuracy, but rather to recognize that LLMs may actually reduce overall error rates when properly implemented rather than introduce new categories of risk.

Human physicians are subject to cognitive biases, fatigue effects, knowledge gaps, and inconsistent application of guidelines that LLMs can help mitigate. An LLM doesn't forget recent literature, doesn't make different decisions based on time of day or patient load, and can systematically cross-reference multiple databases simultaneously. The key insight is that rather than viewing LLMs as uniquely problematic due to hallucinations, we should recognize them as potentially complementary to human decision-making, with different error patterns that might actually enhance overall clinical accuracy.

This perspective is particularly important in obstetrics, where the stakes feel higher due to maternal-fetal considerations, but where systematic literature review capabilities and consistent guideline application could significantly improve care quality. The goal should be leveraging LLMs to reduce the types of errors humans commonly make while maintaining human oversight for the nuanced clinical judgment that obstetric care requires.

Essential Prompt Engineering for ObGyn Practice

Perhaps the most critical skill ObGyn physicians need to develop is sophisticated prompt engineering. The effectiveness of LLMs depends heavily on how questions are framed and what context is provided. This isn't simply about asking clear questions, but about understanding how to structure clinical information in ways that maximize LLM performance while minimizing the risk of misleading responses.

Effective clinical prompting requires presenting patient information systematically, much like presenting a case at rounds, but with additional structure that helps the LLM process information optimally. This means including relevant negatives, specifying gestational age and trimester for pregnancy-related questions, and providing clear context about acuity level and decision-making timeframe. For example, rather than asking "Should this patient get antibiotics?" a well-engineered prompt would specify the clinical scenario, relevant risk factors, timing constraints, and what specific guidance is needed.

Advanced prompting strategies become crucial for complex obstetric scenarios. Chain-of-thought prompting, where you ask the LLM to walk through its diagnostic reasoning step by step, can help identify potential flaws in logic or missing considerations. Multi-step analysis prompting, where you request assessment of maternal risks first, then fetal risks, then integrated recommendations, mirrors good clinical thinking while making the AI's reasoning process transparent and verifiable.

Quality verification prompting represents another essential skill, where physicians learn to ask follow-up questions that probe the limitations and confidence levels of LLM recommendations. Questions like "What additional information would strengthen this assessment?" or "How confident are you in this recommendation and why?" can help identify areas where human expertise is most needed.

The iterative nature of effective prompting also requires skill development. Starting with broad questions and then narrowing based on responses, using follow-up prompts to explore specific aspects, and cross-checking recommendations across different prompt approaches can help ensure robust clinical decision support rather than simple question-and-answer interactions.

Implementation and Future Directions

Successful integration of LLMs into obstetric practice requires acknowledging both their current limitations and future potential. The survey emphasizes that domain-specific training is crucial for medical applications, suggesting that obstetric-specific LLMs will likely be necessary for optimal performance. Current general-purpose models lack the nuanced understanding of pregnancy physiology, labor management, and maternal-fetal medicine that specialized training could provide.

The federated learning approaches discussed in the survey offer particular promise for obstetrics, where patient privacy concerns are paramount but where collaborative learning across institutions could significantly improve model performance. This could enable development of sophisticated clinical decision support tools that learn from diverse patient populations while protecting individual privacy.

Rather than viewing LLMs as a threat to clinical expertise, obstetric providers should consider them as powerful tools for enhancing clinical efficiency, reducing certain types of errors, and improving access to evidence-based care. The key lies in developing the prompt engineering skills necessary to use these tools effectively while maintaining the human oversight essential for safe obstetric practice. As these technologies continue to evolve, physicians who develop these skills early will be better positioned to leverage their benefits while mitigating their limitations.