Impacto de la inteligencia artificial en el diagnóstico y tratamiento médico: una revisión sistemática.
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| Title: | Impacto de la inteligencia artificial en el diagnóstico y tratamiento médico: una revisión sistemática. |
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| Authors: | Monsalve Ospina, Yer Orlando1 yer.monsalve@uniminuto.edu |
| Source: | Revista Médica de Risaralda. ene-jun2026, Vol. 32 Issue 1, p137-153. 17p. |
| Subjects: | ARTIFICIAL intelligence, DIAGNOSIS, CLINICAL decision support systems, EVIDENCE synthesis, THERAPEUTICS, DIAGNOSTIC imaging, MEDICAL technology |
| Abstract (English): | Introduction: Artificial intelligence (AI) has emerged as a tool of growing relevance in modern healthcare. Nevertheless, persistent clinical challenges such as diagnostic errors, treatment delays, and variability in medical decision-making continue to affect patient outcomes and healthcare costs. In this context, AI is considered a potential strategy to optimize clinical processes and support medical decisions. Methods: A protocol was registered in PROSPERO (ID: CRD42024000000). Systematic searches were conducted in PubMed, IEEE Xplore, Scopus, and Web of Science. Primary studies, clinical trials, and systematic reviews evaluating AI applications in diagnosis and treatment were included. Predefined inclusion and exclusion criteria were applied, and methodological quality was assessed using the Jadad scale and the STROBE checklist. Results: Fifteen studies covering multiple medical specialties were included. Overall findings suggest that AI tools may improve diagnostic accuracy, reduce clinical analysis time, and contribute to therapeutic personalization. Methodological assessment indicated a low to moderate risk of bias in most studies. Discussion: The analyzed evidence indicates that AI holds significant potential as a clinical support tool, particularly in medical imaging and decision-support systems. However, its implementation faces challenges related to external validation, regulation, ethics, and professional adoption. Conclusion: Artificial intelligence represents a promising technology for strengthening diagnostic precision and treatment optimization in healthcare. Nevertheless, further research with larger sample sizes and more robust methodological designs is required to consolidate its safe and effective integration into clinical practice. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | Introducción: La inteligencia artificial (IA) ha emergido como una herramienta con creciente relevancia en la atención médica moderna. No obstante, persisten desafíos clínicos como errores diagnósticos, demoras terapéuticas y variabilidad en la toma de decisiones, los cuales impactan en los resultados de los pacientes y en los costos sanitarios. En este contexto, la IA se plantea como una estrategia potencial para optimizar procesos clínicos y apoyar la toma de decisiones médicas. Objetivo: Evaluar el impacto de la inteligencia artificial en la precisión de los diagnósticos médicos, examinando cómo su implementación ha mejorado la exactitud en la detección de enfermedades y condiciones médicas. Metodología: Se registró un protocolo en PROSPERO (ID: CRD42024000000). Se realizaron búsquedas sistemáticas en PubMed, IEEE Xplore, Scopus y Web of Science. Se incluyeron estudios primarios, ensayos clínicos y revisiones sistemáticas que evaluaran aplicaciones de IA en diagnóstico y tratamiento. Se aplicaron criterios de inclusión y exclusión predefinidos, y la calidad metodológica fue evaluada mediante la escala de Jadad y la lista de verificación STROBE. Resultados: Se incluyeron 15 estudios que abarcaron diversas especialidades médicas. En conjunto, los hallazgos sugieren que las herramientas de IA pueden mejorar la precisión diagnóstica, reducir los tiempos de análisis clínico y contribuir a la personalización terapéutica. La evaluación metodológica indicó un riesgo de sesgo bajo a moderado en la mayoría de los estudios. Conclusiones: La inteligencia artificial representa una tecnología prometedora para fortalecer la precisión diagnóstica y la optimización terapéutica en la atención médica. No obstante, se requieren estudios adicionales con mayor tamaño muestral y diseños metodológicos robustos para consolidar su integración segura y efectiva en la práctica clínica. [ABSTRACT FROM AUTHOR] |
| Copyright of Revista Médica de Risaralda is the property of Universidad Tecnologica de Pereira and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | MedicLatina |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: lth DbLabel: MedicLatina An: 194026183 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Impacto de la inteligencia artificial en el diagnóstico y tratamiento médico: una revisión sistemática. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Monsalve+Ospina%2C+Yer+Orlando%22">Monsalve Ospina, Yer Orlando</searchLink><relatesTo>1</relatesTo><i> yer.monsalve@uniminuto.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Revista+Médica+de+Risaralda%22">Revista Médica de Risaralda</searchLink>. ene-jun2026, Vol. 32 Issue 1, p137-153. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22ARTIFICIAL+intelligence%22">ARTIFICIAL intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22DIAGNOSIS%22">DIAGNOSIS</searchLink><br /><searchLink fieldCode="DE" term="%22CLINICAL+decision+support+systems%22">CLINICAL decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22EVIDENCE+synthesis%22">EVIDENCE synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22THERAPEUTICS%22">THERAPEUTICS</searchLink><br /><searchLink fieldCode="DE" term="%22DIAGNOSTIC+imaging%22">DIAGNOSTIC imaging</searchLink><br /><searchLink fieldCode="DE" term="%22MEDICAL+technology%22">MEDICAL technology</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Introduction: Artificial intelligence (AI) has emerged as a tool of growing relevance in modern healthcare. Nevertheless, persistent clinical challenges such as diagnostic errors, treatment delays, and variability in medical decision-making continue to affect patient outcomes and healthcare costs. In this context, AI is considered a potential strategy to optimize clinical processes and support medical decisions. Methods: A protocol was registered in PROSPERO (ID: CRD42024000000). Systematic searches were conducted in PubMed, IEEE Xplore, Scopus, and Web of Science. Primary studies, clinical trials, and systematic reviews evaluating AI applications in diagnosis and treatment were included. Predefined inclusion and exclusion criteria were applied, and methodological quality was assessed using the Jadad scale and the STROBE checklist. Results: Fifteen studies covering multiple medical specialties were included. Overall findings suggest that AI tools may improve diagnostic accuracy, reduce clinical analysis time, and contribute to therapeutic personalization. Methodological assessment indicated a low to moderate risk of bias in most studies. Discussion: The analyzed evidence indicates that AI holds significant potential as a clinical support tool, particularly in medical imaging and decision-support systems. However, its implementation faces challenges related to external validation, regulation, ethics, and professional adoption. Conclusion: Artificial intelligence represents a promising technology for strengthening diagnostic precision and treatment optimization in healthcare. Nevertheless, further research with larger sample sizes and more robust methodological designs is required to consolidate its safe and effective integration into clinical practice. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Spanish) Group: Ab Data: Introducción: La inteligencia artificial (IA) ha emergido como una herramienta con creciente relevancia en la atención médica moderna. No obstante, persisten desafíos clínicos como errores diagnósticos, demoras terapéuticas y variabilidad en la toma de decisiones, los cuales impactan en los resultados de los pacientes y en los costos sanitarios. En este contexto, la IA se plantea como una estrategia potencial para optimizar procesos clínicos y apoyar la toma de decisiones médicas. Objetivo: Evaluar el impacto de la inteligencia artificial en la precisión de los diagnósticos médicos, examinando cómo su implementación ha mejorado la exactitud en la detección de enfermedades y condiciones médicas. Metodología: Se registró un protocolo en PROSPERO (ID: CRD42024000000). Se realizaron búsquedas sistemáticas en PubMed, IEEE Xplore, Scopus y Web of Science. Se incluyeron estudios primarios, ensayos clínicos y revisiones sistemáticas que evaluaran aplicaciones de IA en diagnóstico y tratamiento. Se aplicaron criterios de inclusión y exclusión predefinidos, y la calidad metodológica fue evaluada mediante la escala de Jadad y la lista de verificación STROBE. Resultados: Se incluyeron 15 estudios que abarcaron diversas especialidades médicas. En conjunto, los hallazgos sugieren que las herramientas de IA pueden mejorar la precisión diagnóstica, reducir los tiempos de análisis clínico y contribuir a la personalización terapéutica. La evaluación metodológica indicó un riesgo de sesgo bajo a moderado en la mayoría de los estudios. Conclusiones: La inteligencia artificial representa una tecnología prometedora para fortalecer la precisión diagnóstica y la optimización terapéutica en la atención médica. No obstante, se requieren estudios adicionales con mayor tamaño muestral y diseños metodológicos robustos para consolidar su integración segura y efectiva en la práctica clínica. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Revista Médica de Risaralda is the property of Universidad Tecnologica de Pereira and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.22517/25395203.25850 Languages: – Code: spa Text: Spanish PhysicalDescription: Pagination: PageCount: 17 StartPage: 137 Subjects: – SubjectFull: ARTIFICIAL intelligence Type: general – SubjectFull: DIAGNOSIS Type: general – SubjectFull: CLINICAL decision support systems Type: general – SubjectFull: EVIDENCE synthesis Type: general – SubjectFull: THERAPEUTICS Type: general – SubjectFull: DIAGNOSTIC imaging Type: general – SubjectFull: MEDICAL technology Type: general Titles: – TitleFull: Impacto de la inteligencia artificial en el diagnóstico y tratamiento médico: una revisión sistemática. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Monsalve Ospina, Yer Orlando IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: ene-jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01220667 Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: Revista Médica de Risaralda Type: main |
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