What will it take for AI to work for women’s health in Africa?

While mobile health, often referred to as mHealth, laid the foundations of digital health solutions by connecting African women to care via mobile phones, the complexity of the continent’s health burden now demands a decisive shift from simple access to the predictive, personalized precision of artificial intelligence. But to succeed, essential guardrails must be set up. The landscape of women's health in Africa remains one of urgent, unmet needs. The continent bears a disproportionate burden of global health disparities, most tragically demonstrated by maternal mortality. While sub-Saharan Africa is home to about 16% of the world’s population but accounts for an estimated 70% of all maternal deaths worldwide, where rates can be as high as 442 deaths per 100,000 live births, compared to just 12 in high-income countries. Despite maternal mortality falling 40% in the African region between 2000 and 2023, the pace is insufficient for the 2030 Sustainable Development Goals, demanding a twelvefold increase in the annual reduction rate. Beyond maternal mortality, African women face a widespread illness crisis and die more often from communicable diseases, maternal and perinatal conditions, and nutritional deficiencies. According to the World Health Organization, the African region bears a critical share of the global anemia burden, with an estimated 106 million women affected by the condition, which is frequently driven by iron deficiency. This complex web of challenges underscores the urgent need for interventions that can deliver on the promise of health equity and secure the health and survival of African women. Into this challenging environment, the first wave of digital health (mainly mHealth) emerged in the mid-2000s as a powerful lever. Simple, accessible technologies such as those based on short messaging service, or SMS, and unstructured supplementary service data, or USSD, proved that connection to health services via technology, irrespective of geographical location was possible. Initiatives such as South Africa’s MomConnect, for maternal health, demonstrated that mobile phones could effectively deliver educational reminders, support behavior change, and improve clinic attendance. Using basic technology ensured the widest possible reach that did not require a smartphone or internet. Similarly, the WelTel Kenya1 initiative for HIV care proved that the human aspect of digital health was often its most powerful feature. By sending a simple, interactive weekly SMS, the program significantly improved viral suppression rates, demonstrating that facilitating a basic two-way connection between patients and providers could be as effective as complex adherence protocols. The first round of digital health solutions successfully expanded access to health services, an impact most pronounced for outreach-based interventions such as family planning or immunizations, especially when women had direct ownership of a mobile phone. For instance, mHealth tools have been shown to more than double childhood vaccination rates and, in Ethiopia, increased family planning uptake from 68% to 85%. This success, however, has predominantly been about broadening reach rather than significantly driving systemic efficiencies. Using AI to shift technology to provide precision solutions As Africa eyes the promise of AI, it must confront persistent challenges exposed by mHealth. mHealth revealed that the gender digital divide, which manifests asunequal mobile ownership, low digital literacy, still limits women’s access. While mHealth generated local data, it highlighted the critical need for data privacy and governance regulation to protect information and ensure African datasets are used ethically — a need that is now more pronounced in the AI era. Compounding challenges include a broad lack of reliable infrastructure and logistical hurdles. Issues such as intermittent power or reagent supply disrupt digital diagnostics. These structural limitations, exposed by mHealth, will be amplified in the AI era unless directly confronted. Despite these barriers, the opportunity for AI could be transformative, promising a shift from the connection and information era of mHealth — access solutions — to the prediction and personalization era of AI — scale and precision solutions. For example, while mHealth offers static, one-size-fits-all advice on pregnancy milestones, AI can synthesize individual biomarkers and history to predict complications such as preeclampsia before symptoms even appear. We view the opportunities in three main categories. First, AI significantly augments frontline health workers in diagnostics, addressing specialist shortages — for example, by analyzing digitized cervical cell samples on site to make lifesaving cancer screening accessible. Secondly, AI predictive analytics can analyze complex data to forecast health risks such as preeclampsia or unmet family planning needs, enabling proactive, targeted interventions. Finally, personalized engagement through AI-powered chatbots can provide confidential, interactive support on sensitive topics such as sexual health or HIV, overcoming the hitherto intractable challenges of stigma and distance. A prime example of this evolution is Jacaranda Health’s PROMPTS in Kenya. Building on the simple reach of SMS, PROMPTS now uses an AI-driven help desk to analyze thousands of messages from expectant mothers in real time, using natural language processing to triage questions and instantly flag danger signs such as bleeding or reduced fetal movement that require urgent clinical intervention. AI guardrails AI’s potential in point-of-care diagnostics, predictive risk modeling, or personalized support may not be realized without clear guardrails grounded in the hard-won lessons from mHealth. The first wave of digital tools demonstrated the technology’s inherent neutrality, showing it can either reinforce harmful gender disparities or actively challenge them, which means the key to its impact lies in the design and ethical considerations of its rollout. Future guardrails must mandate the use of diverse African datasets; enforce robust data privacy, a critical safety feature in contexts of digital violence, such as cyberstalking and the nonconsensual sharing of intimate images; and establish clear governance to ensure AI is ethical and equitable. This enabling environment depends on political will to champion tools within national strategies, rather than as parallel donor projects. It also requires sustainable funding and financing models such as social or development impact bonds to prevent projects focused on women’s health from remaining short-lived pilots that never reach sustainable implementation. Crucially, AI tools require deep integration with the health system, and must be codesigned to support frontline workers without creating new burdens. Future investments must prioritize deep, codesigned system integration, which means building AI tools seamlessly into existing health workflows to empower frontline workers and avoid creating new administrative burdens. The second crucial area is accelerating the shift from mHealth connection to prediction and personalization, funding advanced AI models and infrastructure needed to deliver precision health care at scale. Thirdly, resources must be strategically allocated to confront cross-cutting challenges that include establishing robust political, financial, and ethical governance frameworks necessary to clear data deserts and ensure equitable AI deployment. Weaving these investments into AI will make AI the essential tool that clears data deserts and delivers lifesaving care for women in Africa.