Over the last decade, artificial intelligence (AI) has revolutionised patient care significantly, enhancing diagnostic accuracy, treatment personalisation, and overall healthcare efficiency. In recent times, the healthcare ecosystem has also witnessed a greater integration of AI into oncology as well. This has enabled healthcare providers to leverage real-time data and predictive analytics, making informed decisions that propel advancements in medical care for cancer patients.
Cancer is a highly heterogeneous disease, with each patient presenting a unique genetic and molecular profile. Continuous and timely monitoring is critical for cancer patients, who are at higher risk of clinical deterioration. AI's ability to process and analyse complex datasets allows for the development of treatment plans tailored to an individual's specific cancer type and genetic makeup. This personalised approach not only has the potential to enhance treatment efficacy but also can minimise adverse effects, leading to better patient outcomes.
As per the Global Market Insights report, AI in the oncology market is projected to reach $7.6 billion by 2032 driven by rising adoption of AI in cancer diagnosis and treatment. In 2023, the oncology diagnostics segment was valued at $341.4 million.
AI systems excel at analysing complex medical data, including imaging scans and genetic profiles, with accuracy, accelerating the diagnostic process. For instance, in a leading cancer hospital in eastern India, Dozee (a contactless monitoring system) implemented an AI-based identification of early warning score (EWS) to identify clinical deterioration in oncology patients. Over six-months, AI-based tool was attached to 12 beds and updates on vital parameters were documented continuously. Later, it was compared with the standard monitoring tool, i.e. MEWS (modified early warning score) collected every four hours. AI-based EWS could identify critical medical conditions with very high sensitivity when compared with the current standard.
In the last 15-20 years, healthcare professionals have developed strategies for better allocation of human resources, particularly in oncology, focusing on early detection and rapid response. The National Health Authority (NHS) introduced the National Early Warning Score (NEWS) later modifying it to NEWS2 for brain function monitoring. These had been further modified to MEWS by incorporating renal function monitoring.
While the potential of AI in oncology is immense, several challenges remain. Ensuring the accuracy and reliability of AI algorithms, addressing data privacy and security concerns, and integrating AI seamlessly into existing healthcare workflows are issues that need to be addressed.
Despite these bottlenecks, the future of AI in oncology looks promising. Ongoing advancements in AI technologies, coupled with increasing collaboration between technology companies, healthcare providers, and researchers, are paving the way for more innovative solutions.
The writer is senior consultant & head, department of critical care medicine, Tata Medical Centre - Kolkata.
