![]() ![]() AI programs trained to spot disease in a lung X-ray, for example, have sometimes gone astray by zeroing in on the markings used to label the right-hand side of the image 3. They lack a common-sense understanding of how the world works that people have just from living in it. But when it comes to cause and effect, machines are typically at a loss. And computers can use those patterns to make predictions - for instance, that a spot on a lung X-ray indicates a tumour 2. Computers can be trained to spot patterns in data, even patterns that are so subtle that humans might miss them. Part of Nature Outlook: Robotics and artificial intelligenceīhattacharya was stymied by the age-old dictum that correlation does not equal causation - a fundamental stumbling block in artificial intelligence (AI). “I couldn’t say that this specific pattern of binding, or this specific expression of genes, is a causal determinant in the patient’s response to immunotherapy,” he explains. He could identify patterns of genes that correlated to immune response, but that wasn’t sufficient 1. Bhattacharya’s idea was to create neural networks that could profile the genetics of both the tumour and a person’s immune system, and then predict which people would be likely to benefit from treatment.īut he discovered that his algorithms weren’t up to the task. ![]() This form of treatment helps the body’s immune system to fight tumours, and works best against malignant growths that produce proteins that immune cells can bind to. When Rohit Bhattacharya began his PhD in computer science, his aim was to build a tool that could help physicians to identify people with cancer who would respond well to immunotherapy. ![]()
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