Challenges and Ethical Considerations in AI-Driven Drug Delivery: Implications for Clinical Translation (Preprint)
Bibliographic record
Abstract
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 strategies. OBJECTIVE This review aims to explore the transformative role of artificial intelligence (AI) in personalizing drug delivery systems by leveraging genomic, proteomic, and metabolic data. METHODS A comprehensive 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. Inclusion and exclusion criteria were applied, and findings were thematically synthesized. RESULTS AI facilitates real-time data interpretation and personalized therapy through smart nanoformulations, biosensor-enabled monitoring, and deep learning-based pharmacogenomic modeling. Applications in oncology, diabetes, and neurodegenerative disorders show improved treatment outcomes. However, challenges include data security, regulatory constraints, and interpretability of AI models. CONCLUSIONS AI bridges genomics and pharmaceutics, driving precision medicine. Innovations like AI-driven 3D printing and federated learning promise a new era in personalized healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.231 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".