Evidence mapPaperPMID 40428137Full record

ReviewBioengineering (Basel, Switzerland)2025

Opportunities for Artificial Intelligence in Operational Medicine: Lessons from the United States Military.

Nikolai Rakhilin, H Douglas Morris, Dzung L Pham, Maureen N Hood, Vincent B Ho

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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.

Nikolai RakhilinDepartment of Radiology and Bioengineering, Uniformed Services University for Health Science, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA.
H Douglas MorrisDepartment of Radiology and Bioengineering, Uniformed Services University for Health Science, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA.ORCID 0000-0002-7942-3748
Dzung L PhamDepartment of Radiology and Bioengineering, Uniformed Services University for Health Science, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA.
Maureen N HoodDepartment of Radiology and Bioengineering, Uniformed Services University for Health Science, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA.
Vincent B HoDepartment of Radiology and Bioengineering, Uniformed Services University for Health Science, 4301 Jones Bridge Rd, Bethesda, MD 20814, USA.ORCID 0000-0001-6603-8384

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conducted in challenging environments such as disaster or conflict areas, operational medicine presents unique challenges for the delivery of efficient and quality healthcare. It exposes first responders and medical personnel to many unexpected health risks and dangerous situations. To tackle these issues, artificial intelligence (AI) has been progressively incorporated into operational medicine, both on the front lines and also more recently in support roles. The ability of AI to rapidly analyze high-dimensional data and make inferences has opened up a wide variety of opportunities and increased efficiency for its early adopters, notably for the United States military, for non-invasive medical imaging and for mental health applications. This review discusses the current state of AI and highlights its broad array of potential applications in operational medicine as developed for the United States military.

Indexed as

artificial intelligencebioengineeringbiomedical technologydisaster medicinemachine learningmilitary medicine

Identifiers

PMID40428137
PMCPMC12108871

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.