Cancer remains a major cause of mortality worldwide, with approximately 20 million new cases diagnosed each year. This contributes to a substantial financial strain on healthcare systems. Extensive research over several decades has yielded substantial progress in the treatment of cancer patients, encompassing combinational therapy, immunotherapy, and targeted therapy (Kesharwani et al., 2012; Khan et al., 2024; Sheikh et al., 2021; Singh & Kesharwani, 2021).

However, these treatment options are associated with inevitable adverse effects, including the infiltration of solid tumors by immune cells and therapeutic substances. In order to address these limitations, initiatives have been undertaken to create innovative anticancer pharmaceuticals. Nanomedicines are a fusion of medicine and nanotechnology, utilized for the monitoring, diagnosis, and treatment of disorders. These can be integrated into biological machineries, medical equipment, and nano biosensors, and delivered clinically to patients (Chadar, Afsana, et al., 2021; Chadar, Afzal, et al., 2021; Kesharwani et al., 2015, 2022; Park, 2019; Sheikh & Kesharwani, 2021).

The integration of nanotechnology and artificial intelligence (AI) in clinical practice for cancer care offers a promising advancement in addressing the limits of conventional cancer therapy strategies. The resulting synergy of these efforts points to a future in which cancer treatment will prioritize precision, leveraging medications that have been meticulously engineered to address the multifaceted nature of this intricate disease. Each individual possesses a distinct genetic code, resulting in phenotypic differences among the population. It is imperative to acknowledge that each cancer patient exhibits a distinct array of biomarkers and demonstrates varying responses to pharmacological treatments. The observed heterogeneity among patients with different cancer types can be attributed to the accumulation of driver mutations within the tumor. This variability complicates the diagnosis and treatment of cancer. In the current landscape, there is an ongoing need for more effective and personalized therapeutic options to combat cancer, which poses a significant challenge and burden to the healthcare system (Bateson, 2015; Kaushal et al., 2022; Kiyotani et al., 2021).

Using AI and Nanotechnology Together to Improve Cancer Treatment Precision

The integration of artificial intelligence and nanotechnology holds considerable potential for enhancing cancer diagnosis, therapy, and monitoring, a field that stands to benefit greatly from such advancements. The ability to overcome the challenges posed by cancer care, advance towards more effective and personalized treatments, and combine artificial intelligence (AI) with nanoscale intervention precision (Mukheja et al., 2025) is a potential avenue for progress.

Implications of AI for Healthcare Monitoring

The utilization of artificial intelligence in healthcare has seen a marked increase in recent years, with the technology being employed to facilitate real-time patient monitoring, the early detection of complications, and the provision of decision-making support. In the field of oncology, the integration of artificial intelligence (AI) with wearable technologies or imaging platforms facilitates continuous monitoring of patient vitals, treatment responses, and the issuance of alerts to healthcare teams regarding potential adverse events. These advancements facilitate more responsive, data-driven care in both hospital and outpatient settings.

• The Examination of Nanotechnology-Detected Tumor Profiles

It is imperative to comprehend the molecular biology of the patient’s illnesses to ensure the provision of suitable medical treatment. The study and collection of data on the unique molecular signature of cells and tissues is made possible by multi-omics technology, which includes genomics, proteomics, transcriptomics, metabolomics, and epigenomics, among others. The process of customizing treatment plans for cancer patients is also contingent on the utilization of biomarker testing. Single-molecule sequencing (SMS) is a prominent third-generation sequencing method that utilizes nanotechnology to provide a direct view of the DNA molecule (Li et al., 2021; Rosenquist et al., 2022; Schadt et al., 2010).

Presently, a novel set of AI-enabled base callers has been developed, thereby enhancing the efficiency and accuracy of sequencing methodologies. These callers utilize a range of algorithms, including Artificial Neural Networks (ANN), Machine Learning (ML), and Deep Learning (DL) (Silvestre-Ryan & Holmes, 2021; Wick et al., 2019).

• The Function of Artificial Intelligence in Nanomaterial Toxicity Prediction

Utilizing linear regression and Bayesian regularized neural networks, Epa et al. (2012) evaluated the effects of 51 metal oxide NPs with distinct metallic cores and 109 NPs with analogous cores but divergent surface modifications. The models demonstrated a high degree of accuracy in predicting cell death in malignant cells and the uptake of nanoparticles by different cell lines in laboratory experiments (Epa et al., 2012).

However, when considering biosafety and nanotoxicology, it is insufficient to merely examine the apparent toxicity of nanomedicines to cells. In such cases, the employment of nano descriptors, such as microscopic images, is imperative for the delineation of the precise properties or the amalgamation of multiple attributes inherent to a particular nanomedicine (Burello & Worth, 2011; Glotzer & Solomon, 2007). Oladele (2014) developed a classifier model for nanoscale material degradation prediction. This model was created using a genetic algorithm and a support vector machine. When formulating recommendations for their safe use, researchers considered physicochemical properties (Oladele, 2014).

• Predicting the Concentration of Tumor Drugs Using AI

The selection of medications is made exclusively on the basis of omics data and biomarker profiles, with the objective of providing personalized cancer treatment. Achieving a prolonged treatment response, as well as accurately predicting it, are both made more difficult by the dynamic nature of the tumor microenvironment and the emergence of drug resistance (Cheng et al., 2020). Therefore, it is imperative to ascertain whether the medication reaches the intended destination and, if so, at what concentration. A range of artificial intelligence (AI) techniques, grounded in machine learning (ML) and deep learning (DL), have been developed to navigate this complexity. Machine learning (ML) is the field of study concerned with algorithms that can learn patterns from data through training on labeled datasets. Deep learning (DL), a subset of machine learning (ML), employs multiple layers of neural networks to process data, frequently demonstrating superiority in tasks such as image recognition and complex pattern detection without the necessity of manual feature selection. Conversely, deep learning (DL) models do not necessitate such specialized input, as they possess the capability to autonomously extract pertinent features from raw data. The discrepancy in the methodologies employed by traditional machine learning (ML) and deep learning (DL) techniques is pronounced. The utilization of DL methods in problem-solving does not necessitate the availability of critical sample attributes (Soori et al., 2023).

• The Use of AI in Pharmacological Response Prediction

The objective of precision cancer medicine is to accurately predict the most effective pharmacological treatment for each patient’s tumors by analyzing their unique molecular profiles (Leventakos et al., 2019). The establishment of molecular linkages that are disrupted in cancer cells should ideally provide the basis for such predictions. For instance, one such example is the treatment of chronic myelogenous leukemia (CML) using targeted inhibition of the Abl tyrosine kinase gene (Blanco-González et al., 2023). A paucity of knowledge persists concerning the molecular pathways of other cancers, particularly those that result in solid tumors, in comparison to CML (Gupta et al., 2021). Machine learning (ML) is a particularly apt solution for identifying significant relationships in cancer-related datasets. In recent times, a number of machine learning (ML)-based approaches have been developed for this purpose. These algorithms leverage linkages identified in previously published datasets to estimate the optimal treatment outcomes based on the genetic profiles of individual patients’ cancers (Tong et al., 2024).