Development and validation of a new artificial intelligence tool (GeneClin) for the clinical diagnosis of genetic diseases

Abstract

Introduction: Advances in the field of Artificial Intelligence (AI) and Machine Learning (ML) have considerable potential to improve the diagnosis and management of rare genetic diseases, due to the human inability to memorize information on a multitude of these diseases, which AI tools could store, analyze and integrate. Objective: to develop and validate a new AI tool for the clinical diagnosis of genetic diseases. Methods: A prospective, cross-sectional, analytical, observational study was conducted at the application level, with a qualitative-quantitative approach and contributing to a technological development project. It was characterized by four stages: selection of the AI ​​tool, selection of the knowledge base, development of the virtual assistant, validation process and implementation in the clinic. Results: A total of 246 patients with genetic diseases and congenital defects were evaluated. The most predominant genetic category was monogenic genetic syndromes with 223 patients who attended the consultation (90.7%). A success rate of 84.1% was obtained and a success/no success ratio of 4.34. The highest percentage of successes was achieved in monogenic or Mendelian syndromes. There were no significant differences between successes and failures in both chromosomal aberrations and congenital defects of environmental etiology. Conclusions: Through this research, an AI virtual assistant has been validated for the clinical diagnosis of genetic diseases with a high percentage of effectiveness of 84%, which confirms its usefulness to support the clinical diagnosis of cases with genetic diseases.

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