Generative Artificial Intelligence Applications in Reproductive Health: Opportunities and Challenges

1. Introduction to Generative AI in Reproductive Health

The public health systems around the world are dealing with a huge problem of falling birth rates and rising rates of infertility. In this setting, generative AI models, such as large-scale language models (LLMs), offer a new frontier to advance reproductive education, tailor preventive interventions, and make conversations between doctors and patients more effective.

Generative artificial intelligence (GenAI), powered by LLMs like ChatGPT, is increasingly being used in healthcare systems, to provide people with personalized healthcare information when they need it. Also, it has many possible applications in reproductive health, because of the requirement for discretion, inclusion, and accessibility in that field [1].

Reproductive health includes a lot of aspects, like, fertility, birth control, sexually transmitted infections (STIs), menstruation health, and care for pregnant women. GenAI provides a space for people to discuss reproductive health issues in a private, conversational manner. This is valuable because such topics are usually sensitive and often stigmatized. It facilitates the exchange of tailored and accurate health information that can be applied in clinical as well as community settings [2]. Thus, these new technologies, like GenAI, are providing new paths to close the gaps in reproductive health. GenAI can empower people to make informed decisions regarding reproduction and reduce misinformation. It aids people at risk to get help and intervention sooner, by ensuring that accurate and compassionate information is more accessible [3].

AI's role is quickly growing from back-end of healthcare analytics and diagnostics into solutions that patients could use themselves. This is an important development because AI now is not only helping the doctors but also instructing, guiding and empowering the patients in real-time intervention.

2. Importance of Reproductive Health

Sexual and Reproductive health is a significant element of human rights and general well-being. It’s not only about how people can have a family, but also about the amount of control and freedom people have over their bodies and decisions in life.

There is a huge unmet need for sexual and reproductive health care services around the world. The World Health Organization reports that almost 270 million women around the world who want contraception can’t get quality family planning. Millions more also struggle to obtain safe delivery and other critical reproductive care [4].

The United Nations' Sustainable Development Goal 3.7 (SDG 3.7) aims to ensure that everyone has access to reproductive healthcare services by 2030. This includes family planning, educational programs, and require making reproductive health a part of national policy and programs. However, gains are constrained by continuing inequities relating to gender, socio-economic status, geography and cultural taboos [5].

3. Applications for Generative AI in Reproductive Health

Improving Reproductive Education. GenAI can provide 24/7 access to high-quality, real-time reproductive health information in a style that is interactive and easy to use. For instance, AI powered chatbots present on the health website or mobile health application can assist patients with a variety of concerns like new diagnosis information or chronic disease management [6,7,8]. These instruments can interpret the medical language, give suggestions for lifestyle adjustments, and offer advice tailored to your health profile [6-8].

One survey, examining ChatGPT’s reliability for users that are looking at fertility-related decisions, discovered that while users valued the convenience and extensive breadth of coverage, the model also sometimes lacked the depth or nuance they needed for important but sensitive questions. However, it could be a helpful resource added to professional medical support, especially when it comes to assist couples in decision making regarding assisted reproductive technology (ART) treatments options, egg freezing, and timing of the conception [9].

In contrast to conventional search engines, AI technologies including Microsoft Copilot demonstrate superior actionability and understandability scores for infertility queries, with both being beyond the 8th-grade reading level [4]. Findings such as these highlight the need for improved readability and source transparency in AI-mediated health communication [3].

Medication management is another common case. Patients are using generative AI tools to help understand how they should be taking their medications, what dosage they should take, potential interactions, or what the side-effects may be [10].

And AI tools can help people interpret their lab results or radiology reports, by generating explanations that are easier to understand than standard medical jargon.

However, these apps raise significant safety concerns, as their reports are often not 100% accurate. At this point, it is unclear who could potentially be held liable for misinformation generated by AI. This highlights the need for regulatory authority that is needed in these areas.

3.1. Enhancing Health Literacy with Generative AI

AI Health literacy is yet a determinant factor in reproductive health outcomes. Problems with understanding how to use contraceptives, being unaware of symptoms of STIs or misinterpreting test results can lead to serious health consequences.

AI can generate text, sound, images and video content of high quality [11]. What’s more, AI-generated educational content — such as explainers, FAQs, or even video scripts — can be adapted to different literacy levels, cultural norms and patient needs. These tools help reduce the gap in health literacy and help individuals understand complex medical facts [1,3].

For instance, ChatGPT can rephrase complex medical instructions into plain language or simulate different formats (Question and Answer, storytelling, infographics) to suit learning preferences. Additionally, such tools can account for cultural context, local language, and even include visual aids—elements that can be particularly important in multilingual or low-literacy populations. However, although GenAI tools like ChatGPT are promising, they are likely to produce content at a higher reading level than ideal for broad public use [3], and should be revised to make them accessible to persons with low literacy skills [4]

3.2. AI-Driven Innovations in Menstrual Health Management

Generative AI tools can help destigmatize menstruation with honest and effective educational content available in everyday language. These tools rely on user-generated data (e.g., cycle tracking tools) to forecast dates of ovulation, monitor symptoms, or even warn users of potential abnormalities such as polycystic ovary syndrome (PCOS) or endometriosis [12].

Period-tracking apps like Clue and Flo rely on AI technology to provide consumers with personalized insights regarding their menstrual cycles. In response, people can make better-informed decisions about their own reproductive health. Also, by considering several variables (e.g., environmental factors, hormone fluctuation, lifestyle and habits, etc.) AI increases the precision of menstrual cycle tracking. This development enables more accurate predictions and tailored health advice [13].

GenAI tools for menstrual health management go beyond simple cycle monitoring to aid in the diagnosis and treatment of menstrual issues. Such AI platforms could identify irregularities in menstruation cycles, such as those associated with PCOS, or endometriosis, and generate suggestions for tailored therapies [14-16].

Across the world, such AI-powered menstrual health platforms are helping to break taboos by creating a safe environment where people can discuss their period pain, hygiene or reproductive cycles, without the fear of being judged. Customized messages and reminders, produced by predictive analytics, facilitate proactive health management [2].

Such generative platforms can be useful for young adolescents who may be uncomfortable asking questions in public, or for people in regions for whom menstrual health is culturally taboo. The anonymity, accessibility, and reactivity of AI systems offer a great support for menstrual health education and empowerment.

3.3. AI in Assisted Reproduction – In Vitro Fertilization

ART has emerged as one of the most prolific domains for AI development, because natively it is complex, costly and emotionally expensive. AI is helping IVF become more successful by enhancing diagnostics, embryo selection, and prediction of cycle outcome [17].

LLMs might be useful in maximizing the efficiency of initial infertility evaluations. One recent program, “Fast Track to Fertility”, with semi-automated bidirectional messaging was able to halved the time necessary for an initial evaluation [18].

For pre-treatment counselling, AI can create personalized risk profiles and help patients to understand their likelihood of success, the risks, and implications of undergoing IVF. By making this information accessible and understandable, AI facilitates informed consent and reduces patient anxiety [19].

Historically in vitro fertilization (IVF) success rates have been calculated according to age-related, population-based models. However, in women who have not previously undergone in vitro fertilization, ovarian reserve and expected ovarian response are frequently not included in these models.

Models have been refined using data from population and clinic-based data sets. The new developed models can predict one or more outcomes, for example, the number of life births over several IVF cycles [20]. Patients and other stakeholders, such as providers and third parties, are already utilizing these predictive models to assess who can use IVF services and clinic, including shared risk financial programs [21].

The use of large language models for fertility assessment faces unique challenges and remains an experimental domain, as the frameworks for their validation and regulation have not yet been formally established [22,23].

However, caution should be applied when discussing the possibilities of AI in assisted reproductive technologies. A critical approach emphasizes the necessity for broad clinical validation and open and transparent algorithms [19].

3.4. AI in Sexual and Reproductive Health Education

Sexual health education often demonstrates weaknesses in inclusion, accuracy, and accessibility. AI might provide scalable strategies to enhance its quality and accessibility. Chatbots can offer guidance and assistance regarding contraceptive methods, addressing inquiries about efficacy, adverse effects, and appropriate usage. They have the potential to democratize and personalize the sex education curricula.

Chatbots and interactive educational platforms can deliver customized curriculum to individuals depending on criteria such as age, gender identity, cultural background, and particular health issues. AI can offer individuals personalized information and support to make the right choice for reproductive health, for example, to choose the contraceptive technique that best suits their requirements.

Bergam et al. [24] evaluated AI-driven sex education products, observing that users valued their accessibility and comprehensibility. Nevertheless, these tools were deficient both emotional sensitivity and empathy. Users expressed reluctance to depend on AI for deeply personal or emotionally sensitive subjects such as sexual trauma or gender identity.

When we compared official ACOG guidelines on contraception with ChatGPT-4o and Google Gemini generated recommendations, both chatbots received good scores for completeness and relevance, with ChatGPT-4o slightly higher on accuracy [25].

Smartphone applications using artificial intelligence offer women information detailed information and tailored suggestions regarding contraceptive methods, including long-acting reversible contraception, according to their specific preferences and risk factors s [5].

Careful review and validation of chatbot responses is essential to prevent the dissemination of inaccurate information. Nevertheless, as AIs get better at understanding the tone and context of conversation — and as their datasets are diversified and validated by subject matter experts — their role in sexual health education is expected to grow.

3.5. AI in Addressing Inequities in Reproductive Healthcare

Health disparities are deeply entrenched in reproductive care. Marginalized groups — racial and ethnic minorities; members of the LGBTQ community; people in rural areas; and people with disabilities — usually face even more obstacles in receiving quality care. By providing free, scalable, and confidential access to information and support, these technologies are emerging as pivotal tools in bridging gaps in reproductive healthcare delivery. AI chatbots can answer questions in multiple languages and help reduce healthcare disparities among non-English speakers.

A pilot trial of AI chatbots in community health clinics showed increased uptake of sexual and reproductive health services, in areas with restrictive abortion laws or limited clinical resources [2]. Some such systems were able to adapt dynamically to changing guidelines and incorporated inclusive language, thereby expanding reach to marginalized people. They allowed users to ask questions without fear of judgment, provided actionable information on nearby services, and adapted language to be more gender-affirming and inclusive.

In low-resource countries, AI's scalability and adaptability are crucial to improving quality and reducing inequities in healthcare, contributing to SDG 3.7 [5]. For example, the "Nurse Nisa" chatbot, deployed in Kenya and the Democratic Republic of Congo, provides information on contraception and abortion, demonstrating that AI could help fill reproductive health knowledge and access gaps [26].

A study from Bangladesh has reported potential use of data and AI for mapping of health coverage, towards identifying where health service access is needed. The authors noted that AI-powered analyses can enhance the visibility of healthcare disparities, thereby influence health policy and resource allocation, improving access for underserved women in these regions [27].

Recent scoping reviews have provided a broader picture of how health disparities influence access to assisted reproductive technologies. These studies suggest that AI could assist in tracking and analyzing demographic data, enabling healthcare providers to customize services to meet the needs of diverse populations. According to their findings, historically underserved populations have significant obstacles when trying to obtain ART; these obstacles can be overcome by AI solutions used in health education and outreach programs [28,29].

3.6. Optimizing Doctor-Patient Communication

AI tools also enhance communication between patients and healthcare providers. Pre-consultation chatbots or educational modules, however, might assist patients to prepare for clinical encounters by encouraging more targeted exchange and dialogue. Patients who used AI to research their conditions before consultations were more likely to formulate specific questions and to engage confidently in shared decision-making [1].

AI-based summaries, visualizations, and educational content will help the busy clinician make counseling sessions clearer and more efficient. In busy or multilingual practices, this saves time for discussions and emotional support [1].

However, it is critical that AI serves to supplement, rather than replace, the human relationships that are central to collaborative medicine. Trust, empathy, and clinical judgment are essential and irreplaceable elements [30].

4. AI’s Limitations in Sexual and Reproductive Health

Although GenAI holds significant potential in the field of reproductive health, it is important to recognize and address its limitations:

Accuracy and recency: The accuracy of the GenAI models is dependent on the quality of the data used to train them. LLMs could generate out-of-date or incorrect health information if not updated and clinically validated [3].

Bias: AI models trained on un-curated internet data can inadvertently perpetuate biases and stigmatizing language. Many models are trained on Western-centric, gender-normative data, which may not fully represent diverse user identities [2]. AI systems are good at finding trends and patterns and solving problems by looking at past data, but they aren't always creative or aware of their surroundings; they don’t know what they don’t know. Because of this weakness, biases that are already built into them may become further entrenched. And AI systems can be expensive to establish and maintain, and they must be constantly monitored to ensure they are accurate and reliable.

Privacy: Collecting and processing sensitive reproductive health data raises ethical and legal questions, particularly in regions with punitive laws on abortion or sexual behavior [31].

Trust and Transparency: The opaque nature of certain algorithms presents difficulties in validating their recommendations [1].

Accessibility: AI relies on digital literacy and internet access, which are barriers for older individuals, individuals with disabilities, and communities located in resource-limited settings. Generative AI may support reproductive and sexual health, but it is harder to deploy where resources are meager. Additionally, several other issues hinder broad-scale adoption, such as lack of infrastructure, high introduction costs, and the need for extensive provider training.

Regulatory oversight: Safety regulations of the use of AI in sexual and reproductive health are currently under development [31].

5. Conclusions

Generative AI is changing the way we think about reproductive health. It provides individualized, private, and scalable assistance in making decisions, gaining an education, and getting care. AI's function is expanding and includes a wide range of things, including helping people get pregnant, keeping track of their periods, teaching sex education, and making sure everyone has the same access to health care. By getting rid of major impediments to communication, equity, and literacy, it supports global public health goals.

But careful planning is important. GenAI must be used in healthcare systems in ways that put the safety, fairness, and dignity of patients first. It should be used to improve human connection, not replace it, and it should be tested through strict, open, and moral research. The best approach to move forward is with a hybrid strategy that combines GenAI tools with human monitoring.