The Future of Medical Technology: Recent Innovations in Artificial Intelligence and Robotics for More Precise and Efficient Treatment
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Artificial Intelligence, Medical Robotics, Precision Medicine
Abstract
The rapid advancement of artificial intelligence (AI) and robotics has transformed the landscape of medical technology, offering unprecedented precision, efficiency, and personalization in patient care. The integration of AI-driven diagnostics and robotic-assisted surgery has improved clinical decision-making, minimized human errors, and enhanced surgical outcomes. This study aims to explore recent innovations in AI and robotics in the medical field, assess their effectiveness in improving treatment precision, and identify challenges in their widespread adoption. A systematic review methodology was employed, analyzing recent peer-reviewed articles, case studies, and reports from medical institutions and technology developers. The findings indicate that AI-powered diagnostic tools significantly enhance early disease detection, while robotic surgery enables minimally invasive procedures with improved accuracy and reduced recovery times. However, challenges such as ethical concerns, high implementation costs, and regulatory hurdles remain key barriers to full-scale adoption. This study concludes that AI and robotics will play an increasingly vital role in modern medicine, revolutionizing healthcare delivery. Further research should focus on optimizing AI algorithms, addressing ethical considerations, and developing cost-effective solutions to ensure broader accessibility and acceptance in medical practice.
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References
Alderton, G. K. (2023). Artificial intelligence meets medical robotics. Science, 381(6654), 141–141. https://doi.org/10.1126/science.adj3312
Almemari, A. (2024). Establishing Liability in Medical Malpractice Due to Artificial Intelligence and Robotics Based Diagnostic and Therapeutic Interventions. 2024 Global Digital Health Knowledge Exchange and Empowerment Conference: Knowledge Exchange of the State-of-the-Art Research and Development in Digital Health Technologies, Enable and Empower Stakeholders Engaged in Enriching and Enhancing the Patient Healthcare Journey, gDigiHealth.KEE 2024, Query date: 2025-02-02 12:21:02. https://doi.org/10.1109/gDigiHealth.KEE62309.2024.10761723
Ashina, M. (2021). Migraine: Disease characterisation, biomarkers, and precision medicine. The Lancet, 397(10283), 1496–1504. https://doi.org/10.1016/S0140-6736(20)32162-0
Bauer, G. R. (2021). Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM - Population Health, 14(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.ssmph.2021.100798
Berlanga, P. (2022). The European MAPPYACTS Trial: Precision Medicine Program in Pediatric and Adolescent Patients with Recurrent Malignancies. Cancer Discovery, 12(5), 1266–1281. https://doi.org/10.1158/2159-8290.CD-21-1136
Boulos, M. N. K. (2021). Digital twins: From personalised medicine to precision public health. Journal of Personalized Medicine, 11(8). https://doi.org/10.3390/jpm11080745
Campa, F. (2021). Assessment of body composition in athletes: A narrative review of available methods with special reference to quantitative and qualitative bioimpedance analysis. Nutrients, 13(5). https://doi.org/10.3390/nu13051620
Chatzimichail, E. (2024). Transforming the future of ophthalmology: Artificial intelligence and robotics’ breakthrough role in surgical and medical retina advances: A mini review. Frontiers in Medicine, 11(Query date: 2025-02-02 12:21:02). https://doi.org/10.3389/fmed.2024.1434241
Denny, J. C. (2021). Precision medicine in 2030—Seven ways to transform healthcare. Cell, 184(6), 1415–1419. https://doi.org/10.1016/j.cell.2021.01.015
Döhner, H. (2021). Towards precision medicine for AML. Nature Reviews Clinical Oncology, 18(9), 577–590. https://doi.org/10.1038/s41571-021-00509-w
Drevets, W. C. (2022). Immune targets for therapeutic development in depression: Towards precision medicine. Nature Reviews Drug Discovery, 21(3), 224–244. https://doi.org/10.1038/s41573-021-00368-1
Ebers, M. (2024). AI Robotics in Healthcare Between the EU Medical Device Regulation and the Artificial Intelligence Act. Oslo Law Review, 11(1). https://doi.org/10.18261/olr.11.1.2
Gupta, N. (2024). Artificial intelligence (AI) in medical robotics. Advances in Artificial Intelligence: Biomedical Engineering Applications in Signals and Imaging, Query date: 2025-02-02 12:21:02, 141–167. https://doi.org/10.1016/B978-0-443-19073-5.00006-9
Ibrahim, A. (2021). Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods, 188(Query date: 2025-02-02 19:23:59), 20–29. https://doi.org/10.1016/j.ymeth.2020.05.022
Jiang, Y. Z. (2021). Molecular subtyping and genomic profiling expand precision medicine in refractory metastatic triple-negative breast cancer: The FUTURE trial. Cell Research, 31(2), 178–186. https://doi.org/10.1038/s41422-020-0375-9
Johnson, K. B. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
Kumar, P. P. (2023). A Review of Applications of Artificial Intelligence and Robotics in the Medical and Healthcare Sector. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, Query date: 2025-02-02 12:21:02, 49–58. https://doi.org/10.1201/9781003328414-5
Loh, E. (2022). Artificial intelligence for medical robotics. Endorobotics: Design, R and D and Future Trends, Query date: 2025-02-02 12:21:02, 23–30. https://doi.org/10.1016/B978-0-12-821750-4.00002-5
Maceachern, S. J. (2021). Machine learning for precision medicine. Genome, 64(4), 416–425. https://doi.org/10.1139/gen-2020-0131
Manzari, M. T. (2021). Targeted drug delivery strategies for precision medicines. Nature Reviews Materials, 6(4), 351–370. https://doi.org/10.1038/s41578-020-00269-6
McCann, M. R. (2021). L-carnitine and acylcarnitines: Mitochondrial biomarkers for precision medicine. Metabolites, 11(1), 1–21. https://doi.org/10.3390/metabo11010051
Min, J. (2023). Skin-Interfaced Wearable Sweat Sensors for Precision Medicine. Chemical Reviews, 123(8), 5049–5138. https://doi.org/10.1021/acs.chemrev.2c00823
Nicolantonio, F. D. (2021). Precision oncology in metastatic colorectal cancer—From biology to medicine. Nature Reviews Clinical Oncology, 18(8), 506–525. https://doi.org/10.1038/s41571-021-00495-z
Nooraie, R. Y. (2020). Social Network Analysis: An Example of Fusion Between Quantitative and Qualitative Methods. Journal of Mixed Methods Research, 14(1), 110–124. https://doi.org/10.1177/1558689818804060
Padmanabhan, S. (2021). Genomics of hypertension: The road to precision medicine. Nature Reviews Cardiology, 18(4), 235–250. https://doi.org/10.1038/s41569-020-00466-4
Pascual, J. (2022). ESMO recommendations on the use of circulating tumour DNA assays for patients with cancer: A report from the ESMO Precision Medicine Working Group. Annals of Oncology, 33(8), 750–768. https://doi.org/10.1016/j.annonc.2022.05.520
Pasquale, F. A. (2022). THE PRICE OF AUTONOMY: LIABILITY STANDARDS FOR COMPLEMENTARY AND SUBSTITUTIVE MEDICAL ROBOTICS AND ARTIFICIAL INTELLIGENCE. Ius et Praxis, 28(1), 3–19. https://doi.org/10.4067/S0718-00122022000100003
Peirlinck, M. (2021). Precision medicine in human heart modeling: Perspectives, challenges, and opportunities. Biomechanics and Modeling in Mechanobiology, 20(3), 803–831. https://doi.org/10.1007/s10237-021-01421-z
Sainero-Alcolado, L. (2022). Targeting mitochondrial metabolism for precision medicine in cancer. Cell Death and Differentiation, 29(7), 1304–1317. https://doi.org/10.1038/s41418-022-01022-y
Saravanan, S. (2023). Transforming the Medical Landscape: Applications of Robotics and Artificial Intelligence in Combating Infectious Diseases. Global Biosecurity, 5(1). https://doi.org/10.31646/gbio.227
Sharma, K. (2024). PERSONALIZED TELEMEDICINE UTILIZING ARTIFICIAL INTELLIGENCE, ROBOTICS, AND INTERNET OF MEDICAL THINGS (IOMT). Handbook of Research on Artificial Intelligence and Soft Computing Techniques in Personalized Healthcare Services, Query date: 2025-02-02 12:21:02, 301–323. https://doi.org/10.1201/9781003371250-17
Veninga, V. (2021). Tumor organoids: Opportunities and challenges to guide precision medicine. Cancer Cell, 39(9), 1190–1201. https://doi.org/10.1016/j.ccell.2021.07.020
Venkatesh, K. P. (2022). Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00694-7
Xu, H. (2022). Tumor organoids: Applications in cancer modeling and potentials in precision medicine. Journal of Hematology and Oncology, 15(1). https://doi.org/10.1186/s13045-022-01278-4
Yang, S. R. (2022). Precision medicine in non-small cell lung cancer: Current applications and future directions. Seminars in Cancer Biology, 84(Query date: 2025-02-02 19:23:59), 184–198. https://doi.org/10.1016/j.semcancer.2020.07.009
Yilmaz, M. A. (2020). Simultaneous quantitative screening of 53 phytochemicals in 33 species of medicinal and aromatic plants: A detailed, robust and comprehensive LC–MS/MS method validation. Industrial Crops and Products, 149(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.indcrop.2020.112347
Yip, M. (2023). Artificial intelligence meets medical robotics. Science (New York, N.Y.), 381(6654), 141–146. https://doi.org/10.1126/science.adj3312
Yue, F. (2022). Effects of monosaccharide composition on quantitative analysis of total sugar content by phenol-sulfuric acid method. Frontiers in Nutrition, 9(Query date: 2024-12-01 09:57:11). https://doi.org/10.3389/fnut.2022.963318
Zhang, H. (2021). Monogenic diabetes: A gateway to precision medicine in diabetes. Journal of Clinical Investigation, 131(3). https://doi.org/10.1172/JCI142244
Zhong, S. (2021). miRNAs in lung cancer. A systematic review identifies predictive and prognostic miRNA candidates for precision medicine in lung cancer. Translational Research, 230(Query date: 2025-02-02 19:23:59), 164–196. https://doi.org/10.1016/j.trsl.2020.11.012
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Copyright (c) 2024 Rina Farah, Zain Nizam
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