How Al is being harnessed in the treatment of cancer
- As cancer cases rise, Tata Memorial Hospital (TMH) aims to address the shortage of specialists by implementing artificial intelligence (AI).
- The hospital has initiated a project called the 'Bio-Imaging Bank,' leveraging deep learning to create cancer-specific algorithms for early-stage detection.
Bio-Imaging Bank Project
- Objective: To establish a comprehensive repository integrating radiology and pathology images with clinical information, outcome data, and treatment specifics.
- Initially concentrating on head and neck cancers and lung cancers, the project aims to compile data from a minimum of 1000 patients for each cancer type.
- The project involves training and testing AI algorithms for tasks like screening for lymph node metastases, nucleus segmentation and classification, biomarker prediction and therapy response prediction.
- Funded by the Department of Biotechnology, in collaboration with IIT-Bombay, RGCIRC-New Delhi, AIIMS-New Delhi and PGIMER-Chandigarh.
AI in Early Cancer Detection
- AI contributes significantly to cancer detection by emulating the human brain’s information processing.
- In cancer diagnosis, AI analyses radiological and pathological images, learning from extensive datasets to recognise unique features associated with various cancers.
- It facilitates early detection by identifying tissue changes and potential malignancies.
Current Scenario
- TMH has added data from 60,000 patients to the biobank in the last year.
- It has also started using AI to reduce radiation exposure for paediatric patients undergoing CT scans, achieving a 40% reduction in radiation.
- AI algorithms on a pilot basis are also employed in the ICU for thoracic radiology, providing a 98% accuracy in diagnosing conditions related to the chest area.
Future Prospects
- In the future, AI is poised to play a transformative role in cancer treatment, particularly in mitigating fatalities in rural India.
- Doctors envision a future where AI facilitates swift cancer detection with a simple click, potentially eliminating the need for extensive tests.
- AI is anticipated in tailoring treatment approaches based on diverse patient profiles, thereby optimising therapy outcomes.
- Through continuous learning, AI enhances accuracy, ensuring timely cancer diagnoses, improving patient outcomes, and aiding healthcare professionals in decision-making processes.
- However, debates arise about potential replacement of human radiologists, facing regulatory scrutiny and resistance from some doctors and health institutions.