Evidence map›Paper›PMID 42571566›Full record

ArticleCureus2026

Artificial Intelligence in Febrile Neutropenia: From Risk Scores to Real-Time Clinical Decision Support.

Kunj R Ghantiwala, Ruchik Kevadiya, Pradeep Pentapurthy, Michael V Kolar, Vesna Tegeltija

Abstract readEditorial
In one paragraph

Article in Cureus, 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

5 authors.

Kunj R GhantiwalaMedicine, Gujarat Medical Education and Research Society (G.M.E.R.S.) Medical College, Navsari, IND.
Ruchik KevadiyaInternal Medicine, Henry Ford Health System, Rochester Hills, USA.
Pradeep PentapurthyInternal Medicine, Wayne State University/Henry Ford Rochester Hospital, Rochester Hills, USA.
Michael V KolarInternal Medicine, Wayne State University School of Medicine, Rochester Hills, USA.
Vesna TegeltijaInternal Medicine, Wayne State University School of Medicine, Rochester Hills, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Febrile neutropenia remains one of the most urgent and clinically challenging complications in oncology care, where timely recognition and decision-making can significantly influence outcomes. While traditional risk assessment tools have long supported clinicians in stratifying patients, they are limited in their ability to adapt to the complexity and rapidly changing nature of clinical situations. In this context, artificial intelligence (AI) is increasingly being explored as a way to complement existing approaches and support more responsive and individualized care. This editorial discusses how the role of AI in febrile neutropenia is evolving, moving from static risk scoring systems toward the possibility of real-time clinical decision support. It highlights both the potential of these tools to enhance clinical workflows and the practical challenges that still need to be addressed before widespread adoption. As these technologies continue to develop, careful integration into clinical practice may help improve how clinicians assess risk and manage patients with febrile neutropenia.

Indexed as

artificial intelligenceclinical decision support systemsfebrile neutropeniamachine learningoncology

Identifiers

PMID42571566
PMCPMC13452642

What OpenQuestion holds

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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.