How AI will shape the future of precision diagnosis

With its ability to sift through large amounts of data, AI has the potential to make medical examinations more precise, reliable, and efficient. For example, in CT-based lung cancer screening, researchers at Philips have demonstrated that a deep learning algorithm may be helpful to radiologists as a decision support tool or second opinion. Similarly, in digital pathology, algorithms can point to regions of interest in tissue samples that demand further inspection by the pathologist, while making it easier to discard slides without signs of cancer. And by unravelling the molecular mechanisms that give rise to an individual's cancer, we can truly begin to understand how targeted treatment may help that individual. Next to the interpretation of medical data, AI can also support with ancillary tasks such as patient scheduling.