Utilizing AI and Nanotechnology Solutions to Propel Progress in Cancer Diagnostics, Therapeutics, and Integrated Theranostics

Introduction

In spite of the cumulative progress achieved within the medical field, the domains of cancer diagnosis (Jin et al., 2020, Zhang et al., 2019) and treatment continue to exhibit limitations, underdevelopment, and inefficiencies (Jin et al., 2010). Presently employed conventional diagnostic methodologies struggle to identify the earliest indicators of cancer initiation, while conventional treatment protocols are hindered by pronounced toxicity, poor bioavailability, non-specific targeting, and the emergence of multi-drug resistance (Das & J, 2023). Consequently, the prognosis for cancer patients remains bleak, contributing to a persistent annual rise in cancer-related mortality rates (Zhang et al., 2019). Novel, more sophisticated tools need to be harnessed to produce diagnostic techniques of heightened sensitivity, alongside therapies that are targeted, precise, and safe to counteract drug resistance (Das & J, 2023). Nanotechnology offers avenues for innovative approaches, leveraging augmented physicochemical properties and capabilities to facilitate accurate and specific cancer diagnosis that transcends conventional practices (Jin et al., 2020, Zhang et al., 2019). Moreover, nanotechnology facilitates the engineering of delivery vehicles for targeted drug delivery that are markedly more efficient and safer, capable of addressing mechanisms of drug resistance (Das & J, 2023, Jin et al., 2020). Complementarily, artificial intelligence assumes a pivotal role in the analysis of extensive datasets, proving invaluable in the design and advancement of diagnostic and therapeutic paradigms rooted in nanotechnology. Additionally, artificial intelligence enables concurrent analysis of diagnostic data, enabling accurate deductions concerning evaluated cases and providing guidance for treatment monitoring (Das & J, 2023).