Development and validation of a new artificial intelligence tool (GeneClin) for the clinical diagnosis of genetic diseases
Year: 2025
Type: article
Study design: Observational Study
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.
Source: Data & Metadata
DOI: 10.56294/dm2025857
Authors: César Dilú Sorzano, Yohandra Calixto Robert, Yelena Pereira Perera, José Pérez Trujillo, Diana Martín García, Gisel Pérez Breff, Gloria Lidia Peña Martínez, Estela Morales Peralta, Paulina Araceli Lantigua Cruz, Haydee Rodríguez Guas, Melek Dáger Salomón, Margarita Arguelles Arza, Roberto Lardoeyt Ferrer, Rafael Eduardo Montaño Arrieta, Norma Elena De León Ojeda, Laritza Matínez Rey, Dayana Delgado López, Noel Taboada Lugo, Daniel Quintana Hernández, Yamilé Lozada Mengana, João Ernesto
Institution: Universidad Mayor de San Andrés
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4408759921