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Artificial Intelligence in Healthcare and Education · Journal article
Nanomedicine · September 1, 2026
A consensus or society position rather than new primary data.
This narrative review synthesizes literature on AI applications in drug discovery, nanosystem design, antimicrobial development, and nanotoxicology published between 2020 and 2025. It describes AI's potential roles in molecular profiling, predictive modelling, and toxicological assessment but does not report original trial data, clinical outcomes, or quantified efficacy; the source explicitly notes that continued research and clinical translation are necessary.
Narrative review. Literature on AI applications in drug discovery, nanosystem delivery, oncology, antimicrobials, and nanotoxicology published 2020–2025; specific selection criteria and screening methods not stated..
AI combined with high-performance computing enables detailed molecular profiling to support personalized cancer therapies. Machine learning models are applied to design of antimicrobial drugs, prediction of antibacterial efficacy, and strategies to counter resistant strains. AI plays a critical role in nanotoxicology, predicting adverse effects of nanomaterials and interpreting complex toxicological data.
AI plays a critical role in nanotoxicology, predicting adverse effects of nanomaterials and interpreting complex toxicological data. The convergence of AI and nanotechnology provides more accurate diagnostics, tailored treatments, and improved safety evaluations.
This review describes conceptual and computational advances in AI-driven drug discovery and delivery but does not report clinical trial results or patient outcomes. Clinicians should regard these findings as current landscape synthesis requiring downstream validation before adoption.
A narrative review synthesizing AI applications across drug discovery, nanosystems delivery, and disease treatment; descriptive and exploratory rather than reporting original empirical results or clinical outcomes.
As stated by the source record.
This review describes conceptual and computational advances in AI-driven drug discovery and delivery but does not report clinical trial results or patient outcomes. Clinicians should regard these findings as current landscape synthesis requiring downstream validation before adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Artificial Intelligence (AI) has become a fundamental driver of scientific progress, particularly in disease diagnosis, drug development, and drug delivery optimization. The intersection of AI, drug design, and nanosystems for delivery is accelerating the advancement of personalized nanomedicines and innovative diananostic and therapeutic approaches. This narrative review explores the integration of nanotechnology and AI in healthcare, with emphasis on cancer treatment, drug discovery, antimicrobials, and nanotoxicology. Based on studies published between 2020 and 2025, the analysis highlights AI applications in molecular profiling, predictive models for antimicrobial resistance, and nanomaterial safety assessments. In oncology, AI combined with high-performance computing enables detailed molecular profiling, supporting personalized cancer therapies. Machine learning models are also applied to the design of antimicrobial drugs, prediction of antibacterial efficacy, and strategies to counter resistant strains. Furthermore, AI plays a critical role in nanotoxicology, predicting adverse effects of nanomaterials and interpreting complex toxicological data. The convergence of AI and nanotechnology is revolutionizing healthcare by providing more accurate diagnostics, tailored treatments, and improved safety evaluations. AI-driven models enhance drug discovery, optimize delivery systems, and strengthen toxicological assessments. However, continued research is necessary to refine these technologies and ensure their effective translation into clinical practice.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.