Evidence map›Paper›PMID 40106511›Full record

ArticlePLOS global public health2025

On the ethical and moral dimensions of using artificial intelligence for evidence synthesis.

Soumyadeep Bhaumik

Abstract read
In one paragraph

Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

1 author.

Soumyadeep BhaumikMeta-research and Evidence Synthesis Unit, Health Systems Science, The George Institute for Global Health, Sydney, Australia.ORCID https://orcid.org/0000-0001-9579-4453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly being used in the field of medicine and healthcare. However, there are no articles specifically examining ethical and moral dimensions of AI use for evidence synthesis. This article attempts to fills this gap. In doing so, I deploy in written form, what in Bengali philosophy and culture, is the Adda (আড্ডা) approach, a form of oral exchange, which involves deep but conversational style discussion. Adda developed as a form of intellectual resistance against the cultural hegemony of British Imperialism and entails asking provocative question to encourage critical discourse.The raison d'être for using AI is that it would enhance efficiency in the conduct of evidence synthesis, thus leading to greater evidence uptake. I question whether assuming so without any empirical evidence is ethical. I then examine the challenges posed by the lack of moral agency of AI; the issue of bias and discrimination being amplified through AI driven evidence synthesis; ethical and moral dimensions of epistemic (knowledge-related) uncertainty on AI; impact of knowledge systems (training of future scientists, and epistemic conformity), and the need for looking at ethical and moral dimensions beyond technical evaluation of AI models. I then discuss ethical and moral responsibilities of government, multi-laterals, research institutions and funders in regulating and having an oversight role in development, validation, and conduct of evidence synthesis. I argue that industry self-regulation for responsible use of AI is unlikely to address ethical and moral concerns, and that there is a need to develop legal frameworks, ethics codes, and of bringing such work within the ambit of institutional ethics committees to enable appreciation of the complexities around use of AI for evidence synthesis, mitigate against moral hazards, and ensure that evidence synthesis leads to improvement of health of individuals, nations and societies.

Identifiers

PMID40106511
PMCPMC11922218

What OpenQuestion holds

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LicenceCC BY
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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.