Exploring the Potential of ChatGPT for Supporting Family Planning Needs of Postpartum Women in Resource-constrained Areas of India
Pushpendra Singh
Publisher 2
November 21, 2025
Abstract
This study investigates how large language models—specifically ChatGPT—can support individuals seeking information and guidance related to family planning. Through a mixed-methods evaluation involving simulated user queries, expert review, and content analysis, the research examines the accuracy, clarity, and safety of ChatGPT’s responses across a range of family-planning scenarios. Findings indicate that while ChatGPT is capable of delivering accessible explanations and empathetic guidance, its responses vary in consistency and often require refinement to meet clinical communication standards. The results highlight both the promise and the limitations of using AI-assisted tools to enhance reproductive-health education.
About this Publication
As conversational AI becomes more widely used in health information seeking, its role in sensitive domains—such as reproductive health—requires careful examination. This publication explores how ChatGPT performs when addressing questions related to contraceptive options, risks, side effects, reproductive choices, and general family-planning needs.
The study evaluates ChatGPT’s ability to:
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Provide medically aligned and non-judgmental information
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Adapt explanations to different user contexts
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Avoid harmful biases and unsafe recommendations
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Respond consistently across similar prompts
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Support health literacy through simple, clear communication
The publication contributes to the growing body of research exploring digital-health innovation and highlights opportunities for AI-supported tools to complement human-centered care when designed and deployed responsibly.
Methods
The research adopts an exploratory mixed-methods design. A comprehensive set of prompt-based scenarios was developed to reflect typical family-planning questions individuals might ask online or during private consultations.
The methodological approach included:
Prompt-based Evaluation
A collection of diverse reproductive-health questions was submitted to ChatGPT to assess the clarity, specificity, and appropriateness of responses.
Clinical & Expert Review
A panel of reproductive-health experts evaluated the outputs for medical alignment, accuracy, tone, and risk avoidance.
Coding & Thematic Analysis
Responses were analyzed using qualitative coding techniques to identify strengths, gaps, recurring themes, and safety concerns.
Comparative Review
Identical prompts were tested across multiple model versions to examine response consistency and variation.
Together, these methods provided a robust understanding of how generative AI may support—or potentially mislead—users seeking family-planning guidance.
Results
The evaluation revealed that ChatGPT demonstrates meaningful potential to support early-stage information-seeking in family planning. Across most scenarios, the model provided approachable explanations, empathetic language, and structured guidance.
Key findings include:
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Strong general knowledge in describing contraceptive methods, benefits, and risks.
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High clarity and readability, making complex topics accessible to non-specialists.
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Encouraging safety behaviours, such as recommending professional consultation for medical decisions.
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Inconsistency across versions, with some prompts receiving less precise or overly broad responses.
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Occasional omissions, especially around nuanced medical considerations.
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Risk of misinformation when questions require clinical specificity or diagnosis-level detail.
Overall, the results show that ChatGPT can support health literacy and empowerment when used as an informational tool—yet it must be complemented with professional medical guidance.
Discussion
The study demonstrates that conversational AI can be a valuable entry point for individuals seeking sensitive or stigmatized health information, including reproductive choices and family-planning options. ChatGPT excels in its empathetic tone, user-friendly explanations, and ability to reduce barriers for those hesitant to speak directly with clinicians.
However, the research also highlights important caveats:
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AI responses may lack the nuance required for safe medical decision-making.
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Variations across versions can affect reliability.
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Without proper safeguards, users may interpret generalized insights as personalized advice.
For policymakers, designers, and digital-health practitioners, these findings underscore the need for transparent AI systems, human-in-the-loop review models, and ongoing evaluation frameworks. When paired with clear disclaimers and responsible deployment, tools like ChatGPT can strengthen reproductive-health literacy and support better-informed decisions.
References
[1] Mohammad Abolnejadian, Sharareh Alipour, and Kamyar Taeb. 2024. Leveraging ChatGPT for Adaptive Learning through Personalized Prompt-based Instruction: A CS1 Education Case Study. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1-8.
[2] Abiodun I Adanikin, Uche Onwudiegwu, and Olabisi M Loto. 2013. Influence of multiple antenatal counselling sessions on modern contraceptive uptake in Nigeria. The European Journal of Contraception & Reproductive Health Care 18, 5 (2013), 381-387.
[3] Salahuddin Ahmed, Maureen Norton, Emma Williams, Saifuddin Ahmed, Rasheduzzaman Shah, Nazma Begum, Jaime Mungia, Amnesty Lefevre, Ahmed Al-Kabir, Peter J Winch, et al. 2013. Operations research to add postpartum family planning to maternal and neonatal health to improve birth spacing in Sylhet District, Bangladesh. Global Health: Science and Practice 1, 2 (2013), 262-276.
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