Evidence mapPaperPMID 42211539Full record

ArticleFrontiers in digital health2026

AI-driven healthcare: a trend toward better healthcare or the emergence of public health burden.

Virak Sorn, Techly San, Sokchan Lorn

Abstract read
In one paragraph

Article in Frontiers in digital health, 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

3 authors.

Virak SornFaculty of Health Sciences and Biotechnology, University of Puthisastra, Phnom Penh, Cambodia.
Techly SanFaculty of Dentistry, University of Puthisastra, Phnom Penh, Cambodia.
Sokchan LornFaculty of Health Sciences and Biotechnology, University of Puthisastra, Phnom Penh, Cambodia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, artificial intelligence (AI) has become a potent and innovative branch of computer science that is rapidly transforming healthcare delivery across the world. With its promise of improved diagnostics, individualized care, and effective health service management, it has the potential to fundamentally change medical practice and healthcare delivery. Yet, despite its potential, AI's growing influence also raises serious concerns about algorithmic bias, data misuse, over-reliance, and even the high risk of causing the emergence of a public health burden. This article critically examines recent breakthroughs in the application of AI in healthcare, whether AI represents a genuine advance toward better healthcare services or whether it could inadvertently contribute to public health risk. It is then argued that while AI's benefits are undeniable, its unregulated or poorly designed deployment could cause a growing public health burden. A balanced, human-centered, and ethical approach to AI adoption is therefore essential to ensure that digital progress translates into real health gains and discusses the possible future direction of AI-augmented healthcare systems.

Indexed as

AI in healthcarealgorithmic biasartificial intelligenceclinical decisionethics and regulation of AI

Identifiers

PMID42211539
PMCPMC13212299

What Socratic 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 Socratic graph.