Legal and Regulatory Implications of AI Utilization in Drug Development

Artificial intelligence (AI) and machine learning (ML) models have revolutionized the field of medicine by optimizing the protocols required for screening and development of novel phytochemical compounds with the help of trained machine learning models. These machines, along with deep learning generative modeling, enable the efficient study of structural modifications, extraction optimization, and screening of plants’ primary and secondary metabolites. But still these devices must be strictly regulated in context of several ethical and legal concerns, to guarantee their optimum functioning by minimizing the chance of errors. Data protection, data openness, bias, accountability, data privacy, and patenting are the main ethical concerns that are still to be discussed completely in the context of AI use. The main obstacles encountered by AI-assisted devices are the need for larger datasets, including sensitive healthcare data, which ultimately creates concerns that affect doctor-patient trust, and the inability of these tools to justify the procedures, highlighting accountability. Furthermore, regulatory concerns like legalization, ownership, cross-border data transfer, data collection, and optimization protocols are yet to be fully understood, and competent authorities like EMA, FDA, GDPR, HIPAA, HAS, and MHRA are continuously adapting to overcome these issues, because, if unresolved, patient-related data will be under consistent threat. As many of these trained models are already being hacked by the utilization of new cutting-edge technologies, there is a dire need to train these models with robustness to make them against cyberattacks by integrating federated learning and end-to-end encryption. The focus of this chapter is to emphasize the importance of phytochemical compounds that are primarily used in healthcare sector. Moreover, to fully understand the working of AI-assisted devices and models in healthcare sector, we also highlighted that these challenges must be addressed to maintain transparency and fairness in data collection, storage, analysis, and interpretation.