Evidence map›Paper›PMID 42006508›Full record

ArticleEJIFCC2026

Beneficial Intelligence in Laboratory Medicine: Aligning Human and Artificial Intelligence for Value-Based Outcomes.

Damien Gruson, Pradeep Kumar Dabla

Abstract read
In one paragraph

Article in EJIFCC, 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

2 authors.

Damien GrusonDepartment of Laboratory Medicine, Cliniques Universitaires St-Lux, Brussels, Belgium and Université Catholique de Louvain, Brussels, Belgium.
Pradeep Kumar DablaDepartment of Biochemiistry, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, Associated Maulana Azad Medical College, New Delhi, Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into healthcare and laboratory medicine is reshaping diagnostics, workflows, and patient management. Yet, technological progress alone cannot ensure meaningful outcomes. The concept of Beneficial Intelligence (BI), defined as the synergy of human and artificial intelligence (H + A = B), emphasizes that technology must be guided by human purpose, ethics, and empathy. BI reframes AI not as a replacement for human expertise but as an augmentation that enables laboratory professionals to deliver care that is accurate, sustainable, and patient-centered. In alignment with value-based healthcare, BI prioritizes outcomes that matter most-clinical, operational, economic, and societal. Laboratory medicine provides a fertile ground for this framework, where digitalization, automation, and machine learning models already enhance diagnostics, risk stratification, and decision support. However, responsible adoption requires validation against patient outcomes, adherence to structured evaluation frameworks and continuous human oversight. Ultimately, Beneficial Intelligence is not only a technical model but a mindset: a commitment to ensure that the alliance of human wisdom and AI fosters equitable, efficient, and sustainable healthcare for the future.

Indexed as

Artificial IntelligenceBeneficial IntelligenceEthicsOutcomesValue-Based Laboratory Medicine

Identifiers

PMID42006508
PMCPMC13088478

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.