Evidence map›Paper›PMID 37510420›Full record

ArticleHealthcare (Basel, Switzerland)2023

Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers.

Roberto Scendoni, Luca Tomassini, Mariano Cingolani, Andrea Perali, Sebastiano Pilati, Piergiorgio Fedeli

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Plagiarism in Publications: All about Being Fair!The Indian journal of radiology & imaging · 2025
    Article
  2. Review
  3. Article
  4. Article
  5. 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

6 authors.

Roberto ScendoniDepartment of Law, Institute of Legal Medicine, University of Macerata, 62100 Macerata, Italy.ORCID 0000-0003-1910-2405
Luca TomassiniInternational School of Advanced Studies, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0001-8297-7701
Mariano CingolaniDepartment of Law, Institute of Legal Medicine, University of Macerata, 62100 Macerata, Italy.ORCID 0000-0003-1916-1819
Andrea PeraliPhysics Unit, School of Pharmacy, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0002-4914-4975
Sebastiano PilatiPhysics Division, School of Science and Technology, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0002-4845-6299
Piergiorgio FedeliSchool of Law, Legal Medicine, University of Camerino, 62032 Camerino, Italy.ORCID 0000-0003-2287-2582

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and machine learning (ML) span multiple disciplines, including the medico-legal sciences, also with reference to the concept of disease and disability. In this context, the International Classification of Diseases, Injuries, and Causes of Death (ICD) is a standard for the classification of diseases and related problems developed by the World Health Organization (WHO), and it represents a valid tool for statistical and epidemiological studies. Indeed, the International Classification of Functioning, Disability, and Health (ICF) is outlined as a classification that aims to describe the state of health of people in relation to their existential spheres (social, family, work). This paper lays the foundations for proposing an operating model for the use of AI in the assessment of impairments with the aim of making the information system as homogeneous as possible, starting from the main coding systems of the reference pathologies and functional damages. Providing a scientific basis for the understanding and study of health, as well as establishing a common language for the assessment of disability in its various meanings through AI systems, will allow for the improvement and standardization of communication between the various expert users.

Indexed as

artificial intelligenceInternational Classification of Diseases (ICD)International Classification of Functioning (ICF)machine learningpermanent impairment

Identifiers

PMID37510420
PMCPMC10378994

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.