Evidence map›Paper›PMID 42374363›Full record

ArticleBMC medical ethics2026

Exploring the operational challenges of navigating ethical oversight in the era of artificial intelligence: a qualitative study of health research ethics committees in Tanzania.

Lazaro Amon Solomon Haule, Doreen Mloka, Muhsin Aboud

Abstract read
In one paragraph

Article in BMC medical ethics, 2026. 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

3 authors.

Lazaro Amon Solomon HauleDepartment of Bioethics and Health Professionalism, School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences, P.O. Box 65001, Dar Es Salaam, United Republic of Tanzania. lazaro.haule@muhas.ac.tz.ORCID http://orcid.org/0000-0003-2349-9425
Doreen MlokaDepartment of Pharmaceutical Microbiology, School of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar Es Salaam, United Republic of Tanzania.ORCID http://orcid.org/0000-0002-7300-1958
Muhsin AboudDepartment of Surgery, School of Medicine, Muhimbili University of Health and Allied Sciences, Dar Es Salaam, United Republic of Tanzania.ORCID http://orcid.org/0000-0002-4507-7584

Funding

Educating and Developing Bioethicists in Tanzania (ENGAGE)D43TW011809 · FIC · UNIVERSITY OF PENNSYLVANIA · PI ULRICH, CONNIE MARIE · 2021 to 2025
$1.4M
FIC NIH HHS D43 TW011809Fogarty International Center, NIH, USA 1D43TW011809 - 01
6 · The paper itself

Abstract

backgroundHealth Research Ethics Committees (HRECs) play a pivotal role in safeguarding research participants and ensuring ethical conduct. The rapid integration of artificial intelligence (AI) into health research introduces novel ethical and operational complexities, including algorithmic opacity, bias, data governance challenges, and difficulties in post-approval monitoring. However, empirical evidence on how these AI-specific complexities affect HREC operations in Tanzania remains limited. This study explored operational challenges associated with ethical oversight of AI-related health research among HRECs in Tanzania.

methodsAn exploratory qualitative study design was employed, involving 25 participants (15 HREC members and 10 secretariat staff) purposively selected from 10 HRECs across five zones of mainland Tanzania. In-depth interviews were conducted using a semi-structured guide. Data were transcribed, translated, and analyzed using inductive content analysis with thematic interpretation, supported by NVivo software. An audit trail, reflexive journaling, and data source triangulation were used to enhance credibility and trustworthiness.

resultsA total of 28 codes were generated and organized into 10 subthemes and four overarching themes. While general challenges such as limited funding, high workload, and staffing constraints persisted, AI-related protocols introduced additional operational burdens, including difficulties in assessing algorithmic validity, increased reliance on external technical experts, and challenges in reviewing large-scale datasets. Although 50% of secretariat staff had more than five years of experience, participants emphasized that the key limitation was not general experience but insufficient AI-specific technical expertise. Weak post-approval monitoring systems were particularly inadequate for tracking AI-driven studies.

conclusionTanzanian HRECs demonstrate foundational governance capacity but face AI-specific operational and technical challenges that constrain effective oversight. Strengthening AI-focused training, technical advisory mechanisms, digital review systems, and sustainable financing is essential to support the ethical governance of emerging technologies.

Indexed as

Artificial IntelligenceBiomedical ResearchEthics Committees, ResearchEthics, ResearchHumansQualitative ResearchTanzaniaArtificial IntelligenceEthical OversightHealth Research Ethics CommitteesHealth research governanceOperational Challenges

Identifiers

PMID42374363
PMCPMC13579938

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Registered trials

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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.