Evidence map›Paper›PMID 42098357›Full record

ArticleNPJ digital medicine2026

Financial incentives increase uptake and perceived effectiveness of autonomous medical AI, yet patients still seek human reconfirmation.

Haiyang Yang, Tinglong Dai, Risa M Wolf

Abstract read
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

Haiyang YangCarey Business School, Johns Hopkins University, Baltimore, MD, USA.
Tinglong DaiCarey Business School, Johns Hopkins University, Baltimore, MD, USA. dai@jhu.edu.
Risa M WolfCarey Business School, Johns Hopkins University, Baltimore, MD, USA. rwolf@jhu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly used for diagnostic screening. In diabetic retinopathy screening, autonomous AI systems can identify disease without a specialist. However, patient trust in AI remains a challenge, and it remains unknown whether and how financial incentives-commonly used to promote uptake of medical services-can increase patient acceptance of AI-based care. We examined, in a randomized vignette experiment, whether eliminating a copay would increase patients' willingness to choose AI over an eye care professional's (ECP's) exam, and how patients would respond to an AI diagnosis. We conducted a randomized vignette experiment with 248 U.S. adults with type 1 diabetes, recruited online between August and December 2024. Participants were presented with a primary-care vignette in which they were due for annual diabetic eye screening. An autonomous AI tool was available as an alternative to ECP referral. Participants were randomized in a 2×2 design: (1) no copay vs. $50 copay for AI screening; (2) copay waiver sponsored by insurer vs. AI developer. Participants chose between AI or ECP screening, and rated AI effectiveness. Those choosing AI or ECP indicated their willingness to seek a second opinion (from ECP or AI, respectively) after receiving a normal vs. abnormal result. Waiving the copay significantly increased AI selection (81% vs. 43%, p < 0.001); the source of the incentive did not affect choice. No copay also improved perceived AI effectiveness (mean score 3.67 vs. 3.24, p = 0.02). Patients choosing AI (vs. ECP) were far more likely to seek reconfirmation after abnormal (mean 6.69 vs. 2.41) or even normal (3.43 vs. 2.47) results (both p < 0.001). Eliminating cost barriers increases participants' willingness to opt for AI-based screening, but they still seek human reconfirmation. Incentives can promote AI adoption, but integration strategies must address persistent preferences for ECP reconfirmation-even when AI results are normal.

Identifiers

PMID42098357
PMCPMC13424133

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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.