Cancer, a leading cause of death globally, presents unique challenges due to its heterogeneity and complex molecular signatures. Conventional treatments like surgery, radiation, and chemotherapy often lack specificity, leading to adverse side effects and treatment failures. Targeted drug delivery, aiming to increase drug concentration at the tumor site, offers a promising solution. Nanomedicine, utilizing nanoparticles (NPs) as drug carriers, has emerged as a key technology in this area. NPs, due to their size and ability to encapsulate drugs, can improve drug solubility, stability, and targeted delivery. However, the heterogeneity of tumors poses challenges in developing effective and personalized treatment plans. Artificial intelligence (AI), with its ability to analyze large datasets and identify patterns, offers a powerful tool to overcome these limitations. This review explores the current progress and challenges of integrating NPs and AI for targeted drug delivery in cancer therapy, emphasizing the need for more research in this crucial area. The study focuses on showcasing how the combined use of NPs and AI can improve cancer treatment by enhancing drug delivery precision, optimizing treatment strategies, and facilitating more accurate diagnoses.

The review utilized the PRISMA methodology and surveyed a wide range of literature from computer science, biomedical, and biotechnology journals and conferences. Databases such as IEEE, SAGE Journals, Springer, Elsevier, MDPI, Frontiers, and the National Library of Medicine's National Centre for Biotechnology Information were consulted. The focus was on publications from the last ten years, excluding preprints. Approximately 100 out of 150 reviewed publications were included, emphasizing recent applications of NPs in drug delivery, challenges in this field, and the potential of AI intervention. While extensive literature exists on drug delivery, drug design, bionanotechnology, and NPs for targeted delivery, studies directly addressing the convergence of NPs and AI in targeted drug delivery were limited. This prompted a narrative review, focusing on the potential of AI integration in the field, especially for addressing existing challenges and identifying research gaps.

The study adopted a narrative review approach due to the limited number of studies directly focusing on the convergence of nanoparticles (NPs) and artificial intelligence (AI) for targeted drug delivery. The literature search encompassed several databases, including IEEE, SAGE Journals, Springer, Elsevier, MDPI, Frontiers, and the National Library of Medicine. The review focused on publications within the last ten years, excluding preprints. The selection criteria prioritized studies related to drug delivery, nanoparticle applications in targeted drug delivery, AI applications in drug delivery, and the integration of AI and bionanotechnology. From the initial 150 identified works, 100 publications were selected for detailed analysis. The analysis focused on critical themes, including recent applications and challenges in drug delivery, NPs as drug delivery platforms for cancer therapy, and the potential of AI in addressing existing challenges. The review explores various aspects of AI in nanomedicine, including AI's role in optimizing drug discovery and delivery, patient biomarker detection and profiling for targeted drug delivery, and the use of AI in addressing challenges in cancer imaging. The potential of nanorobotics enhanced by AI for improved drug delivery is also examined. The review concludes by discussing future outlooks and challenges in this field, including the limitations of current AI applications and the need for larger datasets for training AI models to improve accuracy and reliability.

The review highlights the significant potential of combining nanoparticles (NPs) and artificial intelligence (AI) to improve targeted drug delivery in cancer therapy. NPs offer advantages in improving drug solubility, stability, and targeted delivery, overcoming limitations of conventional chemotherapy. However, challenges remain, such as the heterogeneity of tumors and the need for personalized treatment plans. AI, with its capability to analyze large datasets and predict patterns, can help address these challenges. Key areas where AI can contribute significantly include: 1. **Biomarker Detection and Profiling:** AI algorithms can analyze data from biomarker sensing NPs (like quantum dots and gold NPs) to create patient-specific disease profiles, aiding in early diagnosis and personalized treatment. This includes identifying specific gene mutations (e.g., KRAS mutations) that affect treatment response. 2. **Optimizing Drug Delivery Systems:** AI can optimize drug delivery parameters, such as drug formulation, manufacturing techniques, storage, and transport to the target site. Neural networks, for example, can be utilized to predict drug behavior and interactions with biological membranes. 3. **Improving Cancer Imaging:** AI-enhanced image analysis can provide more precise information from imaging modalities (PET, CT, SPECT) used to monitor drug delivery and tumor response. Deep learning models, for instance, can analyze holographic images of nanoparticles for size and refractive index determination, improving the efficiency of nanoparticle tracking analysis. 4. **Drug Synergy Prediction:** AI can predict the synergistic effects of drug combinations, enabling the development of more effective treatments with minimized side effects. Various AI models, such as artificial neural networks and deep learning networks, have shown promise in this regard. 5. **Nanorobotics:** AI is being integrated into the design and control of nanorobotics for targeted drug delivery. AI algorithms can optimize the movement and behavior of these nanorobots, improving their efficiency and precision. The review also identifies existing challenges, such as the limited availability of large, comprehensive datasets needed for training robust AI models, the computational power required for complex AI algorithms, and the need for addressing ethical concerns related to AI in healthcare. The lack of standardized protocols and assays for nanoparticle synthesis and characterization is also highlighted as a significant hurdle. Despite these challenges, the convergence of NPs and AI holds significant promise for revolutionizing cancer treatment.

This review demonstrates the considerable potential of integrating artificial intelligence (AI) with nanoparticle (NP)-mediated drug delivery to address the significant challenges in cancer therapy. The ability of AI to analyze complex datasets, predict drug interactions, and optimize treatment strategies complements the advantages of NPs in targeted drug delivery. The findings highlight AI's crucial role in several aspects of cancer treatment, including biomarker detection, improved imaging techniques, and the development of more efficient nanorobotic systems. The integration of AI can personalize cancer treatment by considering individual patient molecular signatures, ultimately improving treatment efficacy and minimizing side effects. Although the review reveals a current scarcity of research directly focusing on this convergence, the demonstrated potential suggests a critical need for further investigation and development in this area. Future research should prioritize the generation and standardization of large-scale datasets for training more robust and reliable AI models. Furthermore, research focusing on the development of more sophisticated AI algorithms and the exploration of novel nanoparticle designs are crucial for realizing the full potential of this combined approach. This combined approach has the potential to improve patient outcomes and addresses the limitations of traditional cancer therapies.

The convergence of nanoparticles and artificial intelligence offers a promising pathway for improving targeted drug delivery in cancer therapy. While significant challenges remain, including the need for more research, larger datasets, and robust AI models, the potential benefits are substantial. Future research should focus on addressing these challenges, developing standardized protocols, and exploring the integration of AI in all stages of drug development and delivery. This combined approach promises to significantly enhance cancer treatment by improving drug efficacy, personalization, and precision.

The primary limitation of this review is the limited number of studies directly addressing the convergence of nanoparticles and artificial intelligence for targeted cancer drug delivery. The narrative review approach, while providing valuable insights, does not allow for rigorous meta-analysis or systematic comparison of different AI techniques. The availability of large, high-quality datasets suitable for training AI models is a significant constraint in the field, impacting the accuracy and generalizability of current AI models. Furthermore, the review primarily focuses on the clinical applications of this technology and doesn't thoroughly investigate the ethical considerations and regulatory challenges that may arise from the integration of AI in cancer therapy.