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I. Alnaimi

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Review Open access Sep 2026

Assessing and Countering Health Misinformation on Social Media to Safeguard Public Trust in Medicine

The spread of health misinformation through social media platforms has transformed into a serious sociomedical threat in undermining public trust on medicine, health professional’s scientific institutions and public health authorities. This manuscript looks into how health-related false or misleading narratives spread on Facebook, Twitter/X, TikTok, YouTube, WhatsApp, and other similar platforms, especially during public health emergencies like the COVID-19 outbreak. Health misinformation has the potential to change one’s risk perceptions, lessening acceptance of vaccination, promoting unsafe treatments, delaying appropriate care-seeking, and reducing adherence to evidence-based recommendations. According to the review, cognitive mechanism refers to the propagation of rumors as misinformation spreaders are often incapable of disarming popular claims with evident truth. This paper also describes specific monitoring approaches, including social listening, text mining and natural language processing, coding frameworks, and citizenscience validation. It will require coordinated action by healthcare professionals, public health institutions, governments, researchers, media organizations and social media platforms. Strategies include open communication about risks, rapid correction of false claims, promotion of credible sources, digital health literacy, ethical content moderation, platform accountability, and community-based engagement. To maintain public confidence in medicine, it is essential to not only correct misinformation but also enhance the credibility, accessibility and responsiveness of health communication systems.

I. Alnaimi, Ibrahim Abdul Jaleel Yamani, A. Alkhatib · European Journal of Prosthod... · 0 citations

Open access Sep 2026

Artificial intelligence in health care and social inequality: will AI reduce or widen health gaps?

Artificial intelligence (AI) could transform health care, particularly in lowresource settings (1). The technology enables new capabilities, ranging from data acquisition to decision support, that amplify ongoing investments in digital technology to improve access to information, diagnostics, treatment, and decision-making (2). As health gaps persist between and within countries, a key question emerges: will deployable AI solutions widen or narrow existing disparities? (3). In many countries, poverty, education, geography, and race constitute risk factors that contribute both to health status and to access to health services and other determinants of health (4). Two competing hypotheses exist (5). The optimistic view holds that AI can reduce these gaps by improving the accuracy of diagnostics and therapeutics and by expanding access to services that would otherwise be out of reach demonstrates that these capabilities can profitably influence health status and survival in under-resourced settings where access to trained human capital, diagnostic devices, and therapeutics is limited (6). The gloomy view maintains that significant mechanisms exacerbate the very gaps that AI might alleviate: 1) the data on which AI training relies often reflects a chronic lack of representation, 2) the post-deployment conditions under which models operate may drift further from those on which models were trained, 3) the infrastructure and knowledge necessary to deploy new capabilities may be absent in the most vulnerable settings, and 4) oversights in governance and use could further endanger already vulnerable groups as the market for AI grows (7, 8).

I. Alnaimi, Ibrahim Abdul Jaleel Yamani, A. Alkhatib · European Journal of Prosthod... · 0 citations