Evidence map›Paper›PMID 39747639›Full record

ArticleNPJ digital medicine2025

Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective.

Mahnoor Ahmed, Tinglong Dai, Roomasa Channa, Michael D Abramoff, Harold P Lehmann, Risa M Wolf

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
–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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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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

6 authors.

Mahnoor AhmedSection on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.
Tinglong DaiCarey Business School, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-9248-5153
Roomasa ChannaDepartment of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
Michael D AbramoffDepartment of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA.ORCID http://orcid.org/0000-0002-3490-0037
Harold P LehmannSection on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-7698-219X
Risa M WolfHopkins Business of Health Initiative, Johns Hopkins University, Baltimore, MD, USA. RWolf@jhu.edu.ORCID http://orcid.org/0000-0001-7674-520X

Funding

Johns Hopkins Training Program in Biomedical Informatics and Data ScienceT15LM013979 · NLM · JOHNS HOPKINS UNIVERSITY · PI CHRISTOPHER G CHUTE, Hadi Kharrazi · 2022 to 2026
$2.2M
Autonomous AI to mitigate disparities for diabetic retinopathy screening in youth during and after COVID-19R01EY033233 · NEI · JOHNS HOPKINS UNIVERSITY · PI WOLF, RISA MICHELLE · 2021 to 2023
$1.9M
Diabetic Retinopathy: Genetics and Neurodegeneration (MSN246458)K23EY030911 · NEI · UNIVERSITY OF WISCONSIN-MADISON · PI CHANNA, ROOMASA · 2020 to 2024
$1.3M
AHRQ HHS R18 HS029350NEI NIH HHS K23 EY030911NEI NIH HHS R01 EY033233NLM NIH HHS T15 LM013979U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) 5K23EY030911-03U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI) R01EY033233
6 · The paper itself

Abstract

Autonomous artificial intelligence (AI) for pediatric diabetic retinal disease (DRD) screening has demonstrated safety, effectiveness, and the potential to enhance health equity and clinician productivity. We examined the cost-effectiveness of an autonomous AI strategy versus a traditional eye care provider (ECP) strategy during the initial year of implementation from a health system perspective. The incremental cost-effectiveness ratio (ICER) was the main outcome measure. Compared to the ECP strategy, the base-case analysis shows that the AI strategy results in an additional cost of $242 per patient screened to a cost saving of $140 per patient screened, depending on health system size and patient volume. Notably, the AI screening strategy breaks even and demonstrates cost savings when a pediatric endocrine site screens 241 or more patients annually. Autonomous AI-based screening consistently results in more patients screened with greater cost savings in most health system scenarios.

Identifiers

PMID39747639
PMCPMC11697205

What OpenQuestion holds

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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.