Evidence map›Paper›PMID 42676774›Full record

ArticleDigital health

AI-driven health misinformation in Somalia's digital era: A neglected crisis demanding global action.

Samar Yusuf Omar, Maryama Ahmed Ali, Abdirahman Mohamed Adan, Ilyas Abdullahi Khalif

Abstract readLetter
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Samar Yusuf OmarFaculty of Health Sciences and Tropical Medicine, Somali National University, Mogadishu, Somalia.ORCID https://orcid.org/0009-0006-0975-7642
Maryama Ahmed AliFaculty of Health Sciences and Tropical Medicine, Somali National University, Mogadishu, Somalia.
Abdirahman Mohamed AdanFaculty of Health Sciences and Tropical Medicine, Somali National University, Mogadishu, Somalia.
Ilyas Abdullahi KhalifFaculty of Health Sciences and Tropical Medicine, Somali National University, Mogadishu, Somalia.ORCID https://orcid.org/0009-0002-7421-8638

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid expansion of artificial intelligence (AI) and generative technologies is transforming global health communication, but it is also accelerating the spread of health misinformation, particularly in fragile and low-resource settings. Somalia, characterized by limited digital health literacy, weak regulatory frameworks, and increasing reliance on social media for health information, represents a highly vulnerable context for AI-driven infodemics. This Letter to the Editor examines the emerging threat of AI-generated health misinformation in Somalia's digital landscape, highlighting its potential to undermine public trust, distort health behaviours, and weaken epidemic preparedness. It also explores the dual role of AI as both a risk amplifier and a potential solution, particularly through automated misinformation detection, digital health education, and multilingual information dissemination. Evidence suggests that generative AI tools can produce highly persuasive false health content at scale, making it difficult for users to distinguish between credible and misleading information. In Somalia, where institutional fact-checking mechanisms and digital governance structures remain underdeveloped, these risks are further intensified. The letter argues for urgent multisectoral action involving policymakers, healthcare institutions, academic bodies, and technology platforms to strengthen digital health literacy, establish AI governance frameworks, and enhance real-time misinformation surveillance. Addressing these challenges is essential to safeguarding public health, strengthening health system resilience, and ensuring equitable access to accurate health information in the digital era.

Indexed as

artificial intelligencedigital healthhealth misinformationinfodemicSomalia

Identifiers

PMID42676774
PMCPMC13527336

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.