Ethical Issues Arising from the Use of AI in Drug Discovery

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Published: Feb 29, 2024
DOI: https://doi.org/10.12681/jpentai.37093
Keywords:
Artificial Intelligence Drug Discovery Algorithm Bias Data Security AI Ethics
Georgia Livieri
PhDc in Bioethics and Health Policy, Cyprus International Institute for the Environmental and Public Health, Department of Health Sciences, Cyprus University of Technology; Research Associate, Cardiovascular Epidemiology and Genetics Research Lab, Cyprus University of Technology.
Charikleia Anastasopoulou
MSc Linguistics; MSc Health Management, Speech Language Pathologist; ED Nurse, General University Hospital of Patras; Member of Hellenic Neuropsychological Society (HNPS).
Konstantina Briola
PhD Candidate in Health Economics, Department of Economics, University of Piraeus; Research Associate, Laboratory of Health Economics and Management, University of Piraeus.
Panagiota Nikolopoulou
BSc, Department of Electrical and Computer Engineering, Democritus University of Thrace.
Iris Panagiota Efthymiou
Adjunct Lecturer, University of Greenwich, UK; Researcher, Laboratory of Health Economics and Management, University of Piraeus, Greece.
Abstract

Lately, the pharmaceutical industry has been implementing artificial intelligence (AI) in research in multiple ways for the purpose of drug discovery. AI has great benefits to offer by performing tasks that a human may not be capable of. However, it also raises many ethical issues that need to be resolved. After describing the general picture, this paper explores the benefits and disadvantages of AI and attempts to find answers to the challenges it poses in order to make sure that its use will be ethical, fair, unbiased and beneficial to people. The challenges addressed below focus on ensuring patients’ safety from biased AI algorithms and violation of their data privacy, the need to train and inform staff properly, as well as the necessity of achieving an optimal level of trust towards AI. In the end, some policy recommendations are being presented, aiming to contribute to the centralized approach and to the balanced sense among the uses of AI. The key to success is always taking into account human agency in order to make AI’s use transparent and robust, while minimizing its risks.

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