Evidence map›Paper›PMID 41459176›Full record

ArticleEJIFCC2025

Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.

Anna Carobene

Abstract read
In one paragraph

Article in EJIFCC, 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

1 author.

Anna CarobeneIRCCS Ospedale San Raffaele, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is driving a profound transformation across the healthcare landscape, with the potential to enhance diagnostic accuracy, optimize clinical decision-making, improve resource allocation, and advance personalized medicine. In public health, AI is redefining infectious disease epidemiology by enabling outbreak forecasting, genomic surveillance, and data-driven policy support, even in the presence of incomplete information. Within clinical laboratories, AI plays a pivotal and expanding role. It facilitates automation of complex workflows, supports diagnostic interpretation, and contributes to analytical performance improvements. Particularly promising is its integration into point-of-care testing, enabling decentralized diagnostics and broader access to timely care, especially in resource-constrained settings. However, these advancements are not without challenges. Concerns regarding algorithmic bias, lack of data representativeness, and risks to privacy and transparency must be carefully addressed. Moreover, the ethical and societal implications of AI are increasingly central. As emphasized by Pope Francis, while AI may accelerate access to knowledge and innovation, it also risks deepening global disparities and promoting a "throwaway culture" that undermines human dignity. His appeal for a "culture of encounter" rooted in equity, justice, and inclusion aligns with the mission of public health and laboratory medicine. This paper, based on the invited lecture delivered at the Clinical Laboratories Artificial Intelligence Revolution (CLAIR) 2025 conference, explores these themes through a critical lens. International scientific societies such as the IFCC are called to foster equitable implementation of AI by promoting access to training, infrastructure, and governance frameworks thus ensuring that AI contributes meaningfully to global health solidarity and equity.

Indexed as

Artificial IntelligenceEthics and GovernanceHealth EquityLaboratory MedicineMachine Learning

Identifiers

PMID41459176
PMCPMC12743338

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.