Evidence map›Paper›PMID 40584716›Full record

SynthesisFrontiers in medicine2025

Standardized clinical assessments and advanced AI-driven instruments used to evaluate neurofunctional deficits, including within biomarker based framework, in Parkinson's disease - human intelligence made vs. AI models - systematic review.

Aurelian Anghelescu, Constantin Munteanu, Aura Spinu, Vlad Ciobanu, Cristina Popescu, Ioana Elena Cioca, Ioana Andone, Simona-Isabelle Stoica, Mihaela Mandu, Ana Rebedea and 5 more

Erratum issuedAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Aurelian Anghelescu *Department of Specific Discipline, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Constantin Munteanu *Teaching Emergency Hospital "Bagdasar-Arseni", Bucharest, Romania.
Aura SpinuDepartment of Clinical Education, Physical and Rehabilitation Medicine, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Vlad CiobanuDepartment of Computer Science, Politehnica University of Bucharest, Bucharest, Romania.
Cristina PopescuDepartment of Clinical Education, Physical and Rehabilitation Medicine, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Ioana Elena CiocaDepartment of Basic Medical Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Ioana AndoneDepartment of Clinical Education, Physical and Rehabilitation Medicine, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Simona-Isabelle StoicaDepartment of Specific Discipline, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Mihaela ManduDepartment of Clinical Education, Physical and Rehabilitation Medicine, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Ana RebedeaTeaching Emergency Hospital "Bagdasar-Arseni", Bucharest, Romania.
Sebastian GiuvaraTeaching Emergency Hospital "Bagdasar-Arseni", Bucharest, Romania.
Alin-Daniel MalaeleaTeaching Emergency Hospital "Bagdasar-Arseni", Bucharest, Romania.
Andreea-Iulia Vladulescu-TrandafirDepartment of Clinical Education, Physical and Rehabilitation Medicine, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Maria-Veronica MorcovDepartment of Basic Medical Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.
Gelu OnoseDepartment of Clinical Education, Physical and Rehabilitation Medicine, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Considering the extensive development of artificial intelligence (AI) facilities, like Generative Pre-Trained Transformer (ChatGPT) 4.o and ChatGPT Scholar, we explored their abilities to conduct a systematic literature review. Using as a specific domain, an attempt to frame/methodize clinical assessment instruments used to evaluate neuro-functional deficits in Parkinson's disease (PD) - including framed through the ICF(-DH) paradigm - for the above-mentioned comparison between human intelligence (HI) and AI, this paper is as well, a follow-up regarding the most actual subject matter of the AI's capabilities evolution in this respect. As well-known clinical-/paraclinical-/functional evaluations, using assessment quantitative (as much as possible) instruments, are basic endeavors for rehabilitation, as they enable setting of appropriate and realistic therapeutic-rehabilitative specific goals. Methods: Within the actual work, we have first achieved a narrative synthesis of the main molecular mechanisms involved in PD pathophysiology, underpinning its clinical appearance and evolution. To fundament our knowledge on an up-to-date information regarding the clinical-functional evaluation tools practiced in PD, we systematically reviewed the literature in this domain, published in the last 6 years, through a PRISMA type method for filtering/selecting the related bibliographic resources. The same keywords combinations/syntaxes have been used contextually, also to dialogize with ChatGPT4.o and ChatGPT. Results: Scholar Applying PRISMA type methodology (HI achieved), we have selected, matching the filtering criteria, 24 articles. Interrogating the two AI above-mentioned models, we obtained quite difficult to be availed/useful - comparative to our HI obtained - outcomes. Thus, when interrogating ChatGPT4.o, ChatGPT Scholar repeatedly, they provided - partially diverse - inappropriate related answers, including ones pending on the interrogator's IP, although they claimed to have this capacity. Discussion: We consider, regarding their capabilities to achieve systematic literature reviews, that neither ChatGPT 4.o nor ChatGPT Scholar still cannot succeed this (yet, they keep improving lately). Additionally, we have consistently extended, including within a narrative related literature review, our 'dialogue" with these two AI facilities regarding their availability to enhance the related evaluation instruments accuracy on neurofunctional assessments within biomarker-based frameworks. So, our research aimed basically to emphasize the main topical data regarding these two important paradigms of knowledge (based on HI and on AI) acquirements - considering the impetuous development of the latter - and thus, possibly to contribute inclusively at improving the actual performances to achieve Systematic Literature Reviews through the PRISMA type method - for the moment still better served by HI.

Indexed as

artificial intelligenceassessment instrumentsChatGPT4.oChatGPT Scholarhuman intelligenceICF framingParkinson’s disease

Identifiers

PMID40584716
PMCPMC12202485

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.