Citation :

Rajesh Munirathnam, 2024. "The Ethics of AI in Pharmaceuticals: Balancing Innovation with Patient Safety and Privacy" ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume 2, Issue 3: 131-146.

Abstract :

Pharmaceutical industry is embracing Artificial Intelligence (AI) to change how drugs are developed, patient treatments tailored, and clinical trials conducted. However, there are a few issues when it comes to the integration of AI, such as patient safety, privacy and ethnic bias in AI. The purpose of this paper is to discuss the ethical aspects of applying artificial intelligence solutions in the sphere of pharmaceuticals with a strong emphasis on the tension between innovation and health risk. The blog explores the consequences of data subjects’ rights to privacy, difficulties in attaining neutral AI solutions, and protective policies imperative for patients’ protection but not for innovation. In this paper, relevant prior work is assessed in the form of a literature review to bring the reader up to speed as to the current status of AI use in the pharmaceutical industry and the opportunities and challenges it presents. In the part of the methodology, the general research approach applied to conduct the analysis of the ethical issues regarding AI in this sector is described. Using a combination of case studies, regulations, and ethical theories, this section presents a framework for ethical AI application in pharmaceuticals. In the paper’s conclusion, there is a list of recommendations for stakeholders and a note that all the processes linked to AI shall be managed incorporating an interdisciplinary approach to avoid detrimental effects on patient safety and privacy.

References :

[1] Mak, K. K., &Pichika, M. R. (2019). Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today, 24(3), 773-780.

[2] Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., &Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241-1250.

[3] Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., ... &Overington, J. P. (2017). A comprehensive map of molecular drug targets. Nature Reviews Drug Discovery, 16(1), 19-34.

[4] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

[5] Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273-286.

[6] Goodman, K. W. (2016). Ethics, medicine, and information technology: Intelligent machines and the transformation of healthcare. Cambridge University Press.

[7] Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.

[8] He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36.

[9] Mak, K. K., Balijepalli, M. K., & Pichika, M. R. (2022). Success stories of AI in drug discovery-where do things stand?. Expert opinion on drug discovery, 17(1), 79-92.Deng, J., Yang, Z., Ojima, I., Samaras, D., & Wang, F. (2022). Artificial intelligence in drug discovery: applications and techniques. Briefings in Bioinformatics, 23(1), bbab430.

[10] Parikh, R. B., Obermeyer, Z., &Navathe, A. S. (2019). Regulation of predictive analytics in medicine. Science, 363(6429), 810-812.

[11] Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92.

[12] Ahuja, A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7, e7702.

[13] Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).

[14] Parikh, R. B., Teeple, S., & Navathe, A. S. (2019). Addressing bias in artificial intelligence in health care. Jama, 322(24), 2377-2378.

[15] Gassmann, O., Reepmeyer, G., & Von Zedtwitz, M. (2008). Leading pharmaceutical innovation. Berlin: Springer. https://doi. org/10, 1007, 978-3.

[16] Bressman, C. (2017). A Prescription for Pharmaceutical's Future: Balancing Industry and Consumer Concerns in Pharmaceutical Drug Development. Minn. L. Rev., 102, 2265.

[17] Mishra, V. (2018). Artificial intelligence: the beginning of a new era in pharmacy profession. Asian Journal of Pharmaceutics (AJP), 12(02).

[18] Zielinski, A. (2021). AI and the future of pharmaceutical research. arXiv preprint arXiv:2107.03896.

[19] Opderbeck, D. W. (2019). Artificial Intelligence in pharmaceuticals, biologics, and medical devices: Present and future regulatory models. Fordham L. Rev., 88, 553.

[20] Rajesh Munirathnam, 2022. "Precision Medicine in Oncology: How Data Science is Revolutionizing Cancer Treatment", ESP Journal of Engineering & Technology Advancements 2(2): 114-124.

[21] Rajesh Munirathnam, 2022. "The Future of Pharmacovigilance: Using Data Science to Predict and Prevent Adverse Drug Reactions", ESP Journal of Engineering & Technology Advancements, 2(4): 130-141.

[22] Rajesh Munirathnam, 2023. "Data Science and Regulatory Affairs: Navigating the Complex Landscape of Drug Approval Processes", ESP International Journal of Advancements in Computational Technology (ESP-IJACT), Volume 1, Issue 1: 96-109.

[23] Rajesh Munirathnam, 2023. "Data-Driven Strategies for Combatting Antimicrobial Resistance: The Role of AI in Developing New Antibiotics", ESP International Journal of Advancements in Computational Technology (ESP-IJACT), Volume 1, Issue 2: 112-125.

[24] Rajesh Munirathnam, 2024. "Blockchain and Data Science in Pharmaceuticals: Enhancing Transparency and Traceability in Drug Supply Chains", ESP International Journal of Advancements in Science & Technology (ESP IJAST) Volume 2, Issue 1: 67-81.

Keywords :

Artificial Intelligence, Pharmaceuticals, Ethics, Patient Safety, Data Privacy, Drug Discovery, Clinical Trials.