INTELIGENCIA ARTIFICIAL EN INVESTIGACIÓN CLÍNICA: REVISIÓN SISTEMÁTICA DE LA LITERATURA
DOI:
https://doi.org/10.56238/arev7n11-332Palabras clave:
Inteligencia Artificial, Investigación Clínica, Aprendizaje Automático, Ensayos Clínicos, Salud PúblicaResumen
La incorporación de la Inteligencia Artificial (IA) en la investigación clínica ha experimentado un crecimiento exponencial en las últimas décadas, en consonancia con los avances en informática y ciencias de la salud. Esta revisión sistemática de la literatura tuvo como objetivo identificar las principales aplicaciones, potencialidades y desafíos del uso de la IA en las distintas etapas de la investigación clínica, incluyendo el descubrimiento de nuevos fármacos, la selección de pacientes, el diseño de ensayos clínicos y el monitoreo de datos en tiempo real. La metodología siguió las recomendaciones de PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), con búsquedas en bases de datos como PubMed, SciELO y Web of Science, considerando publicaciones entre 2020 y 2024. Se seleccionaron 28 estudios que cumplieron los criterios de inclusión. Los resultados indicaron que la IA, especialmente mediante técnicas de aprendizaje automático y aprendizaje profundo, contribuye a reducir el tiempo de desarrollo de medicamentos, mejorar la precisión en la selección de voluntarios y optimizar la seguridad de los pacientes. Además, se observó un creciente uso de algoritmos para el análisis predictivo de resultados clínicos, lo que contribuye a una toma de decisiones más rápida y fundamentada. En conclusión, si bien la IA no reemplaza el rigor metodológico de los ensayos clínicos, se configura como una herramienta complementaria y estratégica para acelerar y mejorar la generación de conocimiento en salud.
Descargas
Referencias
ASKIN, S; DENIS BURKHALTER; C. G; DAKROUNI, S. E. Artificial Intelligence Applied to clinical trials: opportunities and regulatory considerations. Health and Technology, 2023. DOI: https://doi.org/10.1007/s12553-023-00738-2
BARDIN, L. Análise de conteúdo. São Paulo: Edições 70, 2011.
CHUSTECKI, M. Benefits and Risks of AI in Health Care: Narrative Review. Interactive Journal of Medical Research, v. 13, n. 1, 2024. DOI: https://doi.org/10.2196/53616
CORTIAL, L.; MONTERO, V.; TOURLET, S.; et al. Artificial intelligence in drug repurposing for rare diseases: a mini-review. Frontiers in Medicine, v. 11, p. 1404338, 2024. DOI: https://doi.org/10.3389/fmed.2024.1404338
CRUZ RIVERA, S; LIU, X; CHAN, A. W, et al. SPIRIT-AI extension: guidelines for clinical trial protocols for interventions involving artificial intelligence. Nature Medicine, v. 26, p. 1351-1363, 2020. DOI: https://doi.org/10.1038/s41591-020-1037-7
DAYAN, I; ROTH, H. R; ZHONG, A. et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, v. 27, n. 10, p. 1735-1743, 2021. DOI: https://doi.org/10.1038/s41591-021-01506-3
GHASSEMI, M.; OAKDEN-RAYNER, L.; BEAM, A. L. The false hope of current approaches to explainable AI in health care. The Lancet Digital Health, v. 3, n. 11, p. e745-e750, 2021. DOI: https://doi.org/10.1016/S2589-7500(21)00208-9
GHOSH, S; ABUSHUKAIR, H. M; GANESAN, A; et al.Aproveitando a inteligência artificial explicável para correspondência entre pacientes e ensaios clínicos: um estudo piloto de prova de conceito usando ensaios oncológicos de fase I. PLoS ONE, v, 19, n, 10, p. e0311510, 2024. DOI: https://doi.org/10.1371/journal.pone.0311510
HAN, R; ACOSTA, J. N; SHAKERI, Z; et al. Bias, generalizability, and performance of FDA-cleared AI/ML medical devices: analysis of predicate networks. The Lancet Digital Health, v. 6, p. e269-e279, 2024. DOI: https://doi.org/10.1016/S2589-7500(24)00047-5
HE, Q; XIAO, B; TAN, Y, et al. Integrated multicenter deep-learning system for prognostic risk prediction in bladder cancer from histopathology. British Journal of Cancer, v. 130, p. 2286-2296, 2024. DOI: 10.1038/s41698-024-00731-6.
HU, Q; CHEN Y; ZOU D, et al. Predição de eventos adversos a medicamentos usando aprendizado de máquina com base em registros eletrônicos de saúde: uma revisão sistemática e meta-análise. Front. Pharmacol. 15:1497397, 2024. DOI: 10.3389/fphar.2024.1497397
JUMPER, J; EVANS, R; PRITZEL, A. et al. Predição altamente precisa da estrutura de proteínas com AlphaFold. Nature, v. 596, p. 583–589, 2021. DOI: https://doi.org/10.1038/s41586-021-03819-2
KATHER, J. N; PEARSON, A. T; HALAMA, N. et al. O aprendizado profundo pode prever a instabilidade de microssatélites diretamente a partir da histologia no câncer gastrointestinal. Nat Med 25, 1054–1056 (2019). https://doi.org/10.1038/s41591-019-0462-y
LEE, K; MAI, Y; LIU, Z; RAJA, K, et al. CriteriaMapper: automated identification of EHR patients meeting clinical trial eligibility. Scientific Reports, v. 14, p. 15762, 2024. DOI: 10.1038/s41598-024-77447-x.
LIU, X; CRUZ RIVERA, S; MOHER, D, et al. Reporting guidelines for clinical trials evaluating artificial intelligence interventions: CONSORT-AI and SPIRIT-AI. Nature Medicine, v. 26, p. 1364-1374, 2020. DOI: https://doi.org/10.1038/s41591-020-1034-x
LU, X; YANG, C; LIANG, L, et al. Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review. Journal of the American Medical Informatics Association, v. 31, n. 11, p. 2749-2759, 2024. DOI: https://doi.org/10.1093/jamia/ocae243
LIN D, XIONG J, LIU C, et al. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health, v. 3, n. 8, p. e486-e495, 2021. DOI: 10.1016/S2589-7500(21)00086-8.
MURPHY, K; DI RUGGIERO, E; UPSHUR, R, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Medical Ethics, v. 22, p. 14, 2021. DOI: https://doi.org/10.1186/s12910-021-00577-8
PLANA, D; SHUNG, D. L; GRIMSHAW, A. A, S et al. Randomized clinical trials evaluating artificial intelligence–enabled interventions in health care: a systematic review. JAMA Network Open, v. 5, n. 9, e2233946, 2022. DOI: https://doi.org/10.1001/jamanetworkopen.2022.33946
REDDY, S. Explainability and artificial intelligence in medicine. The Lancet Digital Health, v. 4, n. 6, p. e407-e408, 2022. DOI: https://doi.org/10.1016/S2589-7500(22)00029-2
SCHMIDT, J. et al. Mapping the regulatory landscape for AI in healthcare across the EU and UK. npj Digital Medicine, v. 7, p. 104, 2024. DOI: https://doi.org/10.1038/s41746-024-01221-6
SMUCK, M; ODONKOR, C. A; WILT, J. K et al. The emerging clinical role of wearables: factors for successful implementation in healthcare. npj Digital Medicine, v. 4, p. 45, 2021. DOI: https://doi.org/10.1038/s41746-021-00418-3
STOKES, J. M; YANG, K; SWANSON, K, et al. A deep learning approach to antibiotic discovery. Cell, v. 180, n. 4, p. 688-702.e13, 2020. DOI: https://doi.org/10.1016/j.cell.2020.01.021
TAN, S. Y.; SUMNER, J.; WANG, Y.; YIP, A. W. J. Impacts of remote patient monitoring during care transitions: a systematic review. npj Digital Medicine, v. 7, p. 192, 2024. DOI: https://doi.org/10.1038/s41746-024-01182-w
VARADI, M; ANYANGO, S; DESHPANDE M; NAIR, S, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space. Nucleic Acids Research, v. 50, n. D1, p. D439-D444, 2022. DOI: https://doi.org/10.1093/nar/gkac106
VASEY, B; NAGENDRAN, M; CAMPBELL, B. et al. DECIDE-AI: reporting guideline for the early-stage clinical evaluation of decision support systems driven by AI. Nature Medicine, v. 28, p. 924-933, 2022. DOI: https://doi.org/10.1038/s41591-022-01772-9
