Evidence map›Paper›PMID 38887241›Full record

ArticleFrontiers in public health2024

Large language models in physical therapy: time to adapt and adept.

Waqar M Naqvi, Summaiya Zareen Shaikh, Gaurav V Mishra

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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.

Waqar M NaqviDepartment of Interdisciplinary Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, India.
Summaiya Zareen ShaikhDepartment of Neuro-Physiotherapy, The SIA College of Health Sciences, College of Physiotherapy, Thane, India.
Gaurav V MishraDepartment of Radiodiagnosis, Datta Meghe Institute of Higher Education and Research, Wardha, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Healthcare is experiencing a transformative phase, with artificial intelligence (AI) and machine learning (ML). Physical therapists (PTs) stand on the brink of a paradigm shift in education, practice, and research. Rather than visualizing AI as a threat, it presents an opportunity to revolutionize. This paper examines how large language models (LLMs), such as ChatGPT and BioMedLM, driven by deep ML can offer human-like performance but face challenges in accuracy due to vast data in PT and rehabilitation practice. PTs can benefit by developing and training an LLM specifically for streamlining administrative tasks, connecting globally, and customizing treatments using LLMs. However, human touch and creativity remain invaluable. This paper urges PTs to engage in learning and shaping AI models by highlighting the need for ethical use and human supervision to address potential biases. Embracing AI as a contributor, and not just a user, is crucial by integrating AI, fostering collaboration for a future in which AI enriches the PT field provided data accuracy, and the challenges associated with feeding the AI model are sensitively addressed.

Indexed as

Artificial IntelligenceHumansMachine LearningPhysical TherapistsPhysical Therapy Modalitiesartificial intelligenceBioMedLMevidence-based practicelarge language modelsphysical therapyphysical therapy educationrehabilitation

Identifiers

PMID38887241
PMCPMC11182445

What OpenQuestion 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 OpenQuestion graph.