The Role of artificial intelligence in the Development of Innovative Drugs and Therapies for the Future of Health
Abstract
The development of artificial intelligence (AI) technology has made significant contributions to the healthcare field, especially in the development of innovative drugs and therapies. The combination of computational sophistication and AI data analysis has enabled researchers to identify complex patterns in biomedical data, accelerate drug discovery time, and facilitate therapy personalization. This research aims to explore the important role of AI in drug development and innovative therapies to create a better future of healthcare. This involves an analysis of various AI methods and techniques used in drug development as well as the application of AI in personalized therapy for society. This study was conducted by conducting a literature review and analyzing the latest research and developments in the application of AI in drug and therapy development. The results showed that AI has opened new opportunities in drug development by accelerating the process of drug target identification, molecular simulation, and optimization of clinical trials. Meanwhile, in therapeutics, AI enables better personalization through analysis of patient clinical data and prediction of response to specific treatments. This opens up the potential for the development of more effective and targeted therapies. With the development of AI technology, the development of innovative drugs and therapies has become more efficient and effective. The application of AI in healthcare offers the potential to create a more personalized, precise, and comprehensive healthcare future. The collaboration between medical science and AI technology will lead to more innovative and affordable health solutions for the people. Thus, the role of AI in the development of innovative drugs and therapies is recognized as one of the key pillars in creating a better future of healthcare.
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References
Aliffiro Naufal, M., & Muklason, A. (2022). Pengembangan Aplikasi Healthcare Intelligence System Untuk Pemantauan Kesehatan Ibu Dan Anak: Perancangan Aplikasi Frontend. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(2), 1038–1052. https://doi.org/10.35957/jatisi.v9i2.1902
Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032
Barredo Arrieta, A., DÃaz-RodrÃguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in Healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon, G., Carty, C., Chaput, J.-P., Chastin, S., Chou, R., Dempsey, P. C., DiPietro, L., Ekelund, U., Firth, J., Friedenreich, C. M., Garcia, L., Gichu, M., Jago, R., Katzmarzyk, P. T., … Willumsen, J. F. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine, 54(24), 1451–1462. https://doi.org/10.1136/bjsports-2020-102955
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021). AI applications to medical images: From machine learning to deep learning. Physica Medica, 83, 9–24. https://doi.org/10.1016/j.ejmp.2021.02.006
Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018). Explainable artificial intelligence: A survey. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 0210–0215. https://doi.org/10.23919/MIPRO.2018.8400040
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial Intelligence in Healthcare (pp. 295–336). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Al Kurdi, B., & Akour, I. A. (2021). IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review. Future Internet, 13(8), 218. https://doi.org/10.3390/fi13080218
Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., & Ting, D. S. W. (2020). Artificial intelligence for diabetic retinopathy screening: A review. Eye, 34(3), 451–460. https://doi.org/10.1038/s41433-019-0566-0
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120. https://doi.org/10.1126/scirobotics.aay7120
Habibi, A., & Haryati, R. T. S. (2021). ARTIFICIAL INTELLEGENCE IN NURSING: A LITERATURE REVIEW. Jurnal JKFT, 6(2), 8. https://doi.org/10.31000/jkft.v6i2.5614
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020). AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked, 20, 100378. https://doi.org/10.1016/j.imu.2020.100378
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2
Li, J.-P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., Sakomoto, T., Loewenstein, A., Lam, D. S. C., Pasquale, L. R., Wong, T. Y., Lam, L. A., & Ting, D. S. W. (2021). Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Progress in Retinal and Eye Research, 82, 100900. https://doi.org/10.1016/j.preteyeres.2020.100900
Liang, H., Tsui, B. Y., Ni, H., Valentim, C. C. S., Baxter, S. L., Liu, G., Cai, W., Kermany, D. S., Sun, X., Chen, J., He, L., Zhu, J., Tian, P., Shao, H., Zheng, L., Hou, R., Hewett, S., Li, G., Liang, P., … Xia, H. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature Medicine, 25(3), 433–438. https://doi.org/10.1038/s41591-018-0335-9
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0
Rong, G., Mendez, A., Bou Assi, E., Zhao, B., & Sawan, M. (2020). Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering, 6(3), 291–301. https://doi.org/10.1016/j.eng.2019.08.015
Sallam, M. (2023). ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887
Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
Singh, D., Agusti, A., Anzueto, A., Barnes, P. J., Bourbeau, J., Celli, B. R., Criner, G. J., Frith, P., Halpin, D. M. G., Han, M., López Varela, M. V., Martinez, F., Montes De Oca, M., Papi, A., Pavord, I. D., Roche, N., Sin, D. D., Stockley, R., Vestbo, J., … Vogelmeier, C. (2019). Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: The GOLD science committee report 2019. European Respiratory Journal, 53(5), 1900164. https://doi.org/10.1183/13993003.00164-2019
Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47, 101324. https://doi.org/10.1016/j.tele.2019.101324
Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383. https://doi.org/10.1016/j.giq.2018.09.008
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310. https://doi.org/10.1186/s12911-020-01332-6
Ting, D. S. W., Peng, L., Varadarajan, A. V., Keane, P. A., Burlina, P. M., Chiang, M. F., Schmetterer, L., Pasquale, L. R., Bressler, N. M., Webster, D. R., Abramoff, M., & Wong, T. Y. (2019). Deep learning in ophthalmology: The technical and clinical considerations. Progress in Retinal and Eye Research, 72, 100759. https://doi.org/10.1016/j.preteyeres.2019.04.003
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Trenggono, P. H., & Bachtiar, A. (2023). PERAN ARTIFICIAL INTELLIGENCE DALAM PELAYANAN KESEHATAN: A SYSTEMATIC REVIEW. Jurnal Ners, 7(1), 444–451. https://doi.org/10.31004/jn.v7i1.13612
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., Ye, L., Gao, M., Zhou, Z., Li, L., Wang, J., Yang, Z., Cai, H., Xu, J., Yang, L., … Wang, G. (2020). Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell, 181(6), 1423-1433.e11. https://doi.org/10.1016/j.cell.2020.04.045
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