The integration of Artificial Intelligence (AI) technologies in Indian healthcare is set to revolutionize drug related problem (DRP) management, ensuring patient safety and optimizing healthcare outcomes. AI-driven drug discovery will enable precision medicine by tailoring treatments to individual patient profiles, genetics, and environmental factors. Medication management will be greatly improved through AI-powered applications that track and monitor patients' medication schedules, reducing adverse reactions and enhancing treatment effectiveness. AI algorithms will play a vital role in identifying potential drug interactions and safety concerns, offering real-time alerts to healthcare providers. Personalized drug prescriptions and dosage recommendations will minimize medication errors and maximize treatment efficacy. Healthcare chatbots will empower patients with accurate information, enabling them to actively manage their health and make informed decisions. AI-driven data analytics will facilitate population health management, guiding evidence-based decision-making to address public health concerns effectively. Remote healthcare services powered by AI-driven telemedicine platforms will bridge the gap between underserved populations and healthcare facilities, providing equitable access to quality care. Despite the transformative potential, challenges such as data privacy, regulatory compliance, and integration with existing healthcare infrastructure must be addressed responsibly. By embracing AI technologies collaboratively, Indian healthcare will enter an era of enhanced DRP management, leading to improved patient safety, optimized healthcare outcomes, and a more efficient and equitable healthcare system.
| [1] |
Javaid M , Haleem A , Singh RP , et al. Significance of machine learning in healthcare: features, pillars and applications. International Journal of Intelligent Networks. 2022; 3: 58- 73.
|
| [3] |
Bajwa J , Munir U , Nori A , et al. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188–e194. https://doi.org/10.7861/fhj.2021-0095.
|
| [4] |
Bohr A , Memarzadeh K . The rise of artificial intelligence in healthcare applications. In: Artificial Intelligence in Healthcare. Academic Press: Elsevier B.V.; 2020:25–60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2.
|
| [5] |
Khan O , Parvez M , Kumari P , et al. The future of pharmacy: how AI is revolutionizing the industry. Intelligent Pharmacy. 2023; 1 (1): 32- 40.
|
| [6] |
Choudhury A , Asan O . Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform. 2020; 8 (7): e18599.
|
| [7] |
Divya S , Indumathi V , Ishwarya S , et al. Self-diagnosis medical chatbot using artificial intelligence. Journal of Web Development and Web Designing. 2018;3(1):1–7. Available on: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://core.a c.uk/download/pdf/230494941.pdf.
|
| [8] |
Jovanović M , Baez M , Casati F . Chatbots as Conversational Healthcare Services. Department Head; 2019. Available on: https://arxiv.org/pdf/2011.03969.
|
| [9] |
Fernandes JG . Artificial intelligence in telemedicine. In: Artificial Intelligence in Medicine. Switzerland AG: Springer Nature; 2021:1–10. https://doi.org/10.1007/978-3-030-58080-3_93-1.
|
| [10] |
Pacis DMM , Subido EDC , Bugtai NT . Trends in telemedicine utilizing artificial intelligence. AIP Conf Proc. 2018;1933(1):040009.
|
| [11] |
Joshi D , Khursheed R , Gupta S , et al. Biosimilars in oncology: latest trends and regulatory status. Pharmaceutics. 2022; 14: 2721.
|
| [12] |
Kang HN , Thorpe R , Knezevic I , et al. Regulatory challenges with biosimilars: an update from 20 countries. Ann NY Acad Sci. 2021; 1491: 42- 59.
|
RIGHTS & PERMISSIONS
2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
PDF
(317KB)
