Evidence map›Paper›PMID 40939167›Full record

ArticleJournal of medical Internet research2025

The Right to Explanation in AI: In a Lonely Place.

Alycia Noë, Sarah Bouhouita-Guermech, Ma'n H Zawati

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Alycia NoëCentre of Genomics and Policy, McGill University, Montreal, QC, Canada.ORCID https://orcid.org/0000-0003-0642-7833
Sarah Bouhouita-GuermechCentre of Genomics and Policy, McGill University, Montreal, QC, Canada.ORCID https://orcid.org/0000-0001-5942-1748
Ma'n H ZawatiCentre of Genomics and Policy, McGill University, Montreal, QC, Canada.ORCID https://orcid.org/0000-0002-8905-6259

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technology is increasingly being used in decision-making in all fields, particularly in health care. Automated decision-making promises to change medical practice and potentially improve and streamline the provision of health care. Although the integration of artificial intelligence (AI) into medicine is encouraging, it is also accompanied by fears concerning transparency and accountability. This is where the right to explanation has come in. Legislators and policymakers have relied on the right to explanation, a new right guaranteed to those who are affected by automated decision-making, to ease fears surrounding AI. This is particularly apparent in the province of Quebec in Canada, where legislators recently passed Law 5, an act respecting health and social services information and amending various legislative provisions. This paper explores the practical implications of Law 5, and by extension of the right to explanation internationally, in the health care field. We highlight that the right to explanation is anticipated to alter physicians' obligation to patients, namely the duty to inform. We also discuss how the drafting of the legislation on the right to explanation is vague and hard to enforce. This dilutes the potential of the right to explanation to provide meaningful protections for those affected by automated decisions. After all, AI is a complex and innovative technology and, as such, requires complex and innovative policies. The right to explanation is not necessarily the answer.

Indexed as

Artificial IntelligencePatient RightsHumansQuebecalgorithmartificial intelligenceautomated decisionsautomationclinical decision-makingdeep learningdoctorearly detectionearly warningmachine learningoathphysician’s obligationspractical modelpredictive analyticspredictive modelright to explanation

Identifiers

PMID40939167
PMCPMC12475874

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.