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. In fact, the Future Health Index shows that this is where AI already has the largest adoption – pointing to a strong need for improved operational efficiency. By automating the mundane and speeding up workflows, AI can alleviate overburdened physicians.
For example, AI can support planning, execution and processing of MR exams, helping improve the entire workflow. As another example, natural language processing can help to quickly extract and collate patient information from a multitude of medical records, giving multidisciplinary teams the comprehensive overview they need for optimal decision-making.