Artificial intelligence in oncology
There is no denying the presence of computers in our everyday life, whether it’s through phones, personal virtual assistants such as Apple’s Siri and Amazon’s Alexa, or video games. Lately, the interest and development surrounding artificial intelligence (AI) has escalated, and the opportunities to embrace this within the healthcare industry seem to be growing. I’m not talking about conventional robots—although robotic assisted surgery is on the rise—but the inclusion of high-speed, deep learning computers to aid diagnosis and treatment.
The growth of medical knowledge is far from slowing and is expected to double every 73 days by 2020. In 2017, in excess of over 80,000 oncology papers were published, according to indexing service Web of Science. This sheer volume of research makes it impossible for physicians to stay up to date with the latest advances in the field and so clinical decision support systems are built to work alongside doctors to ensure the latest, highest standard of care is being given. For breast cancer alone, there are 69 different approved drugs for standalone treatment. Add this to the number of combination treatments available, and oncologists face a memory test on top of their routine tasks. The focus for AI separates into two sectors—to improve speed and accuracy of diagnosis, and to increase efficiency of drug discovery.
Developed by IBM, Watson Health is an AI clinical decision support system able to analyse data across many health sectors including cardiology, drug discovery, and diabetes. Watson for Oncology (WFO) focuses on rapid diagnosis and optimum treatment proposals for cancer patients. Researchers in India recently found that WFO treatment recommendations for breast cancer patients were concordant with those of an expert panel of oncologists. In 93% of the 638 cases presented for analysis, the tumor board agreed with the WFO proposals. The same tumor board also found concordant results in lung, colon, and rectal cancer. Discrepancies were found in cases where different treatment options were available in India compared to the training location of WFO in the United States.
