Previously submitted to: JMIR Bioinformatics and Biotechnology (no longer under consideration since Apr 20, 2026)
Date Submitted: Jun 14, 2025
Open Peer Review Period: Jul 21, 2025 - Sep 15, 2025
(closed for review but you can still tweet)
Challenges and Ethical Considerations in AI-Driven Drug Delivery: Implications for Clinical Translation
Background:
Background: The integration of artificial intelligence (AI) into personalized medicine is revolutionizing drug delivery by transitioning from the traditional "one-size-fits-all" approach to patient-specific therapeutic states.
Objective:
Objective: This narrative review is to investigate the transformative role of artificial intelligence (AI) in personalizing drug delivery systems by leveraging genomic, proteomic, and metabolic data, with particular emphasis on ethical and regulatory challenges inherent in clinical translation.
Methods:
Methods: A narrative literature review was conducted using electronic databases such as PubMed, Scopus, and Web of Science to examine studies related to AI in pharmacogenomics, smart drug delivery, biosensing technologies, and drug repurposing. Articles published between 2010 and 2025 were retrieved and synthesized thematically. Over 80 peer-reviewed articles were included based on predefined inclusion and exclusion criteria.
Results:
Results: AI facilitates real-time data interpretation and personalized therapy through smart nano formulations, biosensor-enabled monitoring, and deep learning-based pharmacogenomic modelling. Applications in oncology, diabetes, and neurodegenerative disorders show improved treatment outcomes. However, significant ethical challenges were identified, including data security vulnerabilities affecting approximately 60% of AI healthcare systems, algorithmic bias in 45% of reviewed pharmacogenomic models, and regulatory compliance gaps in over 70% of AI-driven drug delivery platforms. Issues of interpretability, equitable access, and patient consent emerged as critical barriers to clinical translation.
Conclusions:
Conclusions: AI bridges genomics and pharmaceutics, driving precision medicine. However, successful clinical integration requires addressing ethical design principles, ensuring equitable implementation, and establishing global regulatory standards. Innovations like AI-driven 3D printing and federated learning promise a new era in personalized healthcare, contingent upon transparent governance frameworks and multistakeholder collaboration.
Clinicaltrial:
None
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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.