Current AI technologies in cancer diagnostics and treatment

Abstract

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized.

Keywords: Artificial intelligence (AI); Cancer; Cancer diagnosis; Deep learning (DL); Machine learning (ML); Precision oncology.

Fig. 1

AI's diverse roles in cancer care, including enhanced diagnosis, personalized treatment, clinical decision support, biomarker discovery, and drug development each contributing to improved precision, speed, and outcomes in oncology through data-driven innovations

Fig. 2

Whole slide images from different cancer tissues are processed using diverse AI models to enable key applications like cancer detection, subtyping, mutation and biomarker prediction, prognostic evaluation, and survival forecasting, advancing precision oncology through deep learning insights

Fig. 3

Integrative pipeline combining gene expression, variant analysis, and AI/ML modeling. It starts with RNA-Seq-based differential gene analysis, followed by morbid variant filtering, multimodal machine learning, and finally outputs predictive models, risk estimations, and disease-specific visual associations for precision medicine

Fig. 4

AI-powered liquid biopsy and genomic technologies for early cancer detection and personalized oncology. It highlights the use of circulating biomarkers (ctDNA, CTCs), next-generation sequencing, and AI/ML models to identify cancer biomarkers and assess individual risk using multi-omics data for precision treatment planning

Fig. 5

AI applications across cancer care workflows from treatment planning and drug recommendation to robotic surgery and drug discovery. AI enhances decision support, enables personalized radiotherapy, assists in surgery, and predicts drug efficacy and resistance, thereby improving precision, outcomes, and therapy development

Fig. 6

Key challenges limiting AI adoption in cancer care: core issues include lack of data standardization, biased training data, and insufficient clinical validation. These nested problems collectively hinder the reliability, generalizability, and clinical utility of AI-driven cancer diagnostics and treatments

Fig. 7

Eight-point conceptual framework for translational AI in oncology. This framework delineates eight critical areas required for effective AI deployment in cancer therapy: Data Acquisition, Preprocessing, Model Development, Internal/External Validation, Deployment & Monitoring, Ethical Considerations, Regulatory Compliance, and Patient-Centric Design. Each one is defined by its prime purpose, primary challenges, and strategic needs. All these dependent factors make up an end-to-end handbook for AI development toward safe, ethical, and equitable clinical release in oncology

Fig. 8

The future landscape of AI in healthcare, focusing on four key domains: improving patient care through personalization, safeguarding data privacy, addressing ethical dilemmas in AI use, and training healthcare professionals to effectively implement and govern new technologies with responsibility and skill