Artificial intelligence (AI) is reshaping pharmaceutical research by enabling data-intensive tasks to be performed with unprecedented speed and accuracy. While oral delivery remains the most common route of administration, it is dominated by a “one-size-fits-all” paradigm that fails to accommodate inter-individual variability, often leading to suboptimal efficacy or adverse reactions. AI offers a path toward personalised delivery by integrating patient-specific data to predict dose requirements and guide the development of bespoke dosage forms. When coupled with three-dimensional printing (3DP), AI-driven workflows enable decentralised, on-demand production of personalised medicines. This review examines advances of machine learning (ML) in enabling dose prediction, formulation optimisation, and digital manufacturing. It highlights emerging opportunities alongside challenges in data quality, regulatory acceptance, and clinical implementation. We discuss the need for collaboration between academia, industry, and regulators to establish interoperable datasets and robust quality-by-design frameworks. Together, these developments point toward a future in which oral drug delivery is increasingly precise, adaptive, and patient-centred.
Abdalla et al. (Sun,) studied this question.