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Updated: May 8, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia
Published on: May 1, 2021
The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment
Anuja Mishra1, Srishti Sharma1, Swaroop Kumar Pandey1
1Department of Biotechnology, Institute of Applied Sciences & Humanities, GLA University, Mathura, 281406, India.
Abstract:
An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.
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