ARTIFICIAL INTELLIGENCE IN CLINICAL RESEARCH: A SYSTEMATIC LITERATURE REVIEW
DOI:
https://doi.org/10.56238/arev7n11-332Keywords:
Artificial Intelligence, Clinical Research, Machine Learning, Clinical Trials, Public HealthAbstract
The integration of Artificial Intelligence (AI) into clinical research has grown exponentially in recent decades, keeping pace with advancements in computer science and healthcare. This systematic literature review aimed to map the main applications, potential benefits, and challenges of using AI in different stages of clinical research, including drug discovery, eligible patient selection, clinical trial design, and real-time data monitoring. The methodology followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), with searches conducted in databases such as PubMed, SciELO, and Web of Science, considering publications from 2020 to 2024. Forty-eight studies that met the inclusion criteria were selected. The results indicated that AI, particularly through machine learning and deep learning techniques, has contributed to reducing drug development time, improving the accuracy of participant screening, and enhancing patient safety. Furthermore, there was an observed increase in the use of algorithms for predictive analysis of clinical outcomes, contributing to faster and more informed decision-making. In conclusion, while AI does not replace the methodological rigor of clinical trials, it is a complementary and strategic tool for accelerating and improving the generation of knowledge in the field of health.
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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
