Evidence map›Paper›PMID 42624406›Full record

ArticleClinical medicine (London, England)2026

The economic imperative for an AI Productivity Index in healthcare: Beyond clinical promise to fiscal accountability.

Yazan A Al-Ajlouni, Basile Njei

Abstract read
In one paragraph

Article in Clinical medicine (London, England), 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

2 authors.

Yazan A Al-AjlouniMontefiore Medical Center/Einstein School of Medicine, Bronx, NY, United States; Yale International Medicine Program, Yale University, New Haven, CT, United States.
Basile NjeiYale International Medicine Program, Yale University, New Haven, CT, United States; Section of Digestive Diseases, Department of Medicine, Yale University, New Haven, CT, United States; Artificial Intelligence Programme, University of Cumbria, Carlisle, United Kingdom; Ohio University Heritage College of Osteopathic Medicine, Athens, OH, United States; Yale Liver Center, Yale New Haven Health, New Haven, CT, United States; Engelhardt School of Global Health and Bioethics, Euclid University, Bangui, Central African Republic. Electronic address: basile.njei@yale.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly embedded in healthcare delivery, yet its evaluation remains dominated by technical performance metrics that inadequately capture real-world system value. Clinical accuracy is necessary but insufficient to justify adoption in resource-constrained health systems facing workforce shortages and rising demand. AI technologies compete for limited financial, technical and cognitive resources, but there is no standardised framework to assess their effects on productivity, workflow, downstream utilisation or equity. This article argues that treating clinical validity as a proxy for value risks misallocating scarce resources and undermining trust in digital transformation. We propose an AI Productivity Index to complement existing safety, efficacy and economic assessments by evaluating operational impact, implementation burden, opportunity costs and post-deployment consequences. Embedding productivity measurement into procurement and governance processes could help align AI innovation with fiscal accountability, equitable access and sustainable healthcare delivery.

Indexed as

Artificial intelligenceClinical workflowDigital health evaluationHealthcare productivityHealth system efficiencyHealth technology assessment

Identifiers

PMID42624406
PMCPMC13572302

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.