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 Citation: Scendoni, R.; Tomassini, L.; Cingolani, M.; Perali, A.; Pilati, S.; Fedeli, P. Artificial Intelligence in Evaluation of Permanent Impairment: NewOperational Frontiers. Healthcare 2023, 11, 1979. https:// doi.org/10.3390/healthcare11141979 Academic Editors: Pierpaolo Di Lorenzo and Massimo Niola Received: 13 June 2023 Revised: 1 July 2023 Accepted: 7 July 2023 Published: 8 July 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 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.

Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers

Roberto Scendoni;Mariano Cingolani;
2023-01-01

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 Citation: Scendoni, R.; Tomassini, L.; Cingolani, M.; Perali, A.; Pilati, S.; Fedeli, P. Artificial Intelligence in Evaluation of Permanent Impairment: NewOperational Frontiers. Healthcare 2023, 11, 1979. https:// doi.org/10.3390/healthcare11141979 Academic Editors: Pierpaolo Di Lorenzo and Massimo Niola Received: 13 June 2023 Revised: 1 July 2023 Accepted: 7 July 2023 Published: 8 July 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 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.
2023
MDPI
Internazionale
https://www.mdpi.com/2227-9032/11/14/1979
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11393/315290
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