The earlier editorial, by E. Qama, had made a compelling case that tools such as ChatGPT can help people manage chronic conditions only if those people possess what has come to be called prompt literacy: the ability to frame effective queries and then critically interpret what the model returns. In that framing, prompt literacy functions as an extension of digital health literacy, and gaps in it become a new source of disparity. Users who prompt skillfully can shape a plan suited to their own situation, while weaker prompting tends to return advice so generic, or so inaccurate, that acting on it becomes risky. Saad and Yudkin accept the observation as valuable, but they argue it rests on a hidden assumption that collapses precisely where the stakes are highest.

That assumption is access. Qama’s argument implicitly presumes the user has a healthcare system available as an alternative or complement to AI. Where that presumption fails, the authors contend, the disparity that matters most is not a gap in skill but a gap in access. They distinguish between what they call a second-order inequality, differences among users who already have a choice between AI and professional care, and a first-order inequality, the question of whether any accessible guidance exists at all. In low- and middle-income countries, and among under-resourced populations inside wealthy countries, the comparison confronting many patients is not AI versus a clinician. It is AI versus nothing. Under that comparison, even an imperfect AI response may represent a genuine improvement over the counterfactual.

The distinction is not merely philosophical; it maps onto the empirical literature in revealing ways. A scoping review by Alon and Levkovich covering 27 studies of generative AI and health information seeking found that credibility and trust concerns were the most commonly reported barrier, appearing in 48.1 percent of included studies. The same review found that people typically wove AI into a broader information routine rather than treating it as a replacement for other sources, and patients managing low back pain used AI-generated information while seeking professional oversight as the perceived stakes rose. These are exactly the patterns a prompt-literacy framework predicts, because they describe populations for whom professional care remains reachable. The framework, in other words, was built from the experience of people who already have options.

The geographic footprint of the evidence is itself part of the problem. In the Alon and Levkovich review, the largest shares of studies came from the United States, with eight, followed by Saudi Arabia, the United Kingdom, and China, with three each. The reviewers explicitly noted a concentration of evidence from English-speaking and high-income settings, with limited representation from low- and middle-income countries, sub-Saharan Africa, Latin America, and South and Southeast Asia. Only one study came from an African country, a Nigerian study by Adeyemi and colleagues, and its own authors cautioned that their online-recruited convenience sample of university students likely overrepresented people with greater internet access and digital literacy. The theoretical picture of who uses health AI, and why, has been assembled largely from the connected and the insured.

What evidence does exist from under-resourced populations suggests that structural factors, not literacy alone, drive the pattern. A survey of United States users by Ayo-Ajibola and colleagues found that ChatGPT users were more likely than nonusers to report lower income and lower educational attainment, and they also scored lower on a validated eHealth literacy scale. Other studies indicate that chatbots can reduce embarrassment when people seek information about sensitive symptoms, an effect most pronounced for embarrassing sexual symptoms. Yet acceptance cannot be assumed. McLeod and colleagues found that underserved United Kingdom participants valued immediacy and privacy in online support but often rejected chatbots specifically, citing scripted responses and a preference for a live human. Research with older Black adults shows that credibility, cultural tailoring, and historic mistrust of medical institutions shape how chatbot design is received. Taken together, these findings point to access-adjacent and structural forces as the dominant explanatory variables.

If access rather than skill is the binding constraint for many users, the policy problem changes shape entirely. Saad and Yudkin argue that the harm attributed to low prompt literacy in an access-constrained setting is not only the harm of using AI less effectively than a skilled peer; it is also the harm of having only one channel of guidance and using it imperfectly. These are not equivalent problems and they do not share a solution. The former can be addressed primarily through user training. The latter demands improving what the AI channel offers by default: uncertainty signaling that tells users when the model is guessing, red-flag escalation that pushes urgent symptoms toward emergency care, and safer design for users with no route to verification anywhere else. A poorly formed question answered by a well-designed system can still be survivable; a well-formed question answered by a brittle system, with no fallback, may not be.

The equity implications run deeper still. Prompt literacy describes a graded inequality operating on a shared floor of care access. Where that floor does not exist, because of geography, cost, or local health system capacity, the more consequential inequality may be binary rather than graded: whether a person has any accessible source of guidance at all. Treating both situations as the same literacy gap, the authors warn, risks systematically under-weighting the more severe access disparity. The same problem infects intervention design. Prompt literacy workshops embedded in rehabilitation curricula or delivered through existing care programs presuppose contact with a healthcare system. Where that contact is scarce, such interventions may fail to reach the very people who need them most, a design flaw the authors compare to teaching navigation skills to people who have no boat.

The commentary therefore proposes an access-sensitive agenda in which safety is built into the tool and its surrounding delivery infrastructure rather than delegated to the user’s ability to ask the right question. On tool design, that means uncertainty signaling, red-flag symptom escalation, plain-language outputs, multilingual and low-literacy interfaces, and voice-based options. On infrastructure, it means low-bandwidth design, offline-compatible features where feasible, and transparent data and privacy protections. Clinical localization calls for alignment with local guidelines and context-specific medication and referral warnings adapted to what services actually exist nearby. Community delivery would enlist community health workers, clinics, hospitals, pharmacies, schools, universities, libraries, non-governmental organizations, and public health agencies, while referral pathways would connect the chatbot’s output to real escalation options, from emergency care to hotlines and community-based supports. Evaluation, crucially, should measure baseline care access, affordability, appointment availability, travel time, language concordance, and whether AI was used before, instead of, or after professional care.

For researchers, the message is that access constraint should not be treated as one barrier among many but as a variable that changes the meaning of outcomes already being measured. Intention to use, verification behavior, and downstream escalation to a clinician all mean something different for someone who can see a doctor tomorrow than for someone who cannot see one at all. Future studies, the authors urge, should stratify AI adoption and reliance by insurance status, affordability, geographic distance, provider-patient concordance, and sequencing relative to professional care, and should test which access-sensitive designs reduce harm and improve appropriate escalation in real-world self-management. The authors are careful to close the loop on the original argument: prompt literacy remains a useful construct, and better-formed queries do produce more useful outputs while critical interpretation remains essential. Their claim is simply narrower and, in an era when conversational AI is racing into the hands of people the health system has left behind, more urgent. Skill shapes the quality of advice; access determines whether any advice exists. Public health, they argue, cannot afford to confuse the two.

Subject of Research: Healthcare access versus prompt literacy in AI-supported health self-management for low-resource populations

Article Title: Access before literacy: reframing conversational AI in health self-management for low-resource contexts

Article References: Saad, A., & Yudkin, J. S. (2026). Access before literacy: reframing conversational AI in health self-management for low-resource contexts. International Journal of Public Health, 71, Article 1610302. https://doi.org/10.3389/ijph.2026.1610302

Image Credits: AI Generated

DOI: 10.3389/ijph.2026.1610302

Keywords: conversational AI, health self-management, health disparities, healthcare access, prompt literacy, low- and middle-income countries, digital health literacy, chatbots, public health, health equity, generative AI, scoping review