Evidence map›Paper›PMID 41291143›Full record

ReviewNPJ digital medicine2025

Academia loses its grip on digital health solutions as innovation in artificial intelligence shifts towards industry.

Baraa A Hijaz, Olivia M Burke, Sara Y Khattab, Yevgeniy R Semenov

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2025. 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

4 authors.

Baraa A HijazDepartment of Dermatology, Massachusetts General Hospital, Boston, MA, USA.
Olivia M BurkeDepartment of Dermatology, Massachusetts General Hospital, Boston, MA, USA.
Sara Y KhattabDepartment of Dermatology, Massachusetts General Hospital, Boston, MA, USA.
Yevgeniy R SemenovDepartment of Dermatology, Massachusetts General Hospital, Boston, MA, USA. ysemenov@mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past decade, healthcare tools leveraging artificial intelligence and machine learning have transitioned from academic-centered development to industry. Key drivers include limited funding, rising computational requirements, and an exodus of academic talent to industry. The ramifications of this are both significant and concerning. Strategies should focus on sharing capital, talent, and advisory boards across industry and academia to foster innovation that is clinically relevant and impactful.

Identifiers

PMID41291143
PMCPMC12764846

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.