Cognitive computer assistants are helping clinicians to make diagnostic and therapeutic decisions. They evaluate medical data much faster, while delivering at least the same level of precision. It is hardly surprising, therefore, that, applications with Artificial Intelligence are being used more frequently.
Hospitals and doctors’ surgeries have to deal with huge volumes of data: X-ray images, test results, laboratory data, digital patient records, OR reports, and much more. To date, they have mostly been handled separately. But now the trend is towards bringing everything into a single unified software framework. This data integration is not only enabling faster processing of medical data and creating the basis for more efficient interworking between the various disciplines. It is also promising to deliver added value. New, self-learning computing algorithms will be able to detect hidden patterns in the data and provide clinicians with valuable assistance in their diagnostic and therapeutic decision-making.
Better diagnosis thanks to Artificial Intelligence: 30 times faster than a doctor with an error rate of 1%.
Source: PwC
Analysing tissue faster and more accurately
“Artificial Intelligence and robotics offer enormous benefits for our day-to-day work,” asserts Prof. Dr Michael Forsting, Director of the Diagnostic Radiology Clinic of the University Hospital in Essen. The clinic has used a self-learning algorithm to train a system in lung fibrosis. After just a few learning cycles, the computer was making better diagnoses than a doctor: “Artificial Intelligence is helping us to diagnose rare illnesses more effectively, for example. The reasons are that – unlike humans – computers do not forget what they have once learned, and they are better than the human eye at comparing patterns.”
Especially in the processing of image data, cognitive computer assistants are proving helpful in relieving clinicians of protracted, monotonous and recurring tasks, such as accurately tracing the outlines of an organ on a CT scan. The assistants are also capable of filtering information from medical image data that a clinician would struggle to identify on-screen.
Artificial Intelligence diagnosis – Better than the doctor
These systems are now even surpassing humans, as a study at the University of Nijmegen in the Netherlands demonstrates: the researchers assembled two groups to test the detection of cancerous tissue. One comprised 32 -developer teams using dedicated AI software solutions; the other comprised twelve pathologists. The AI developers were provided in advance with 270 CT scans, of which 110 indicated dangerous nodes and 160 showed healthy tissue. These were intended to aid them in training their systems. The result: the best AI system attained virtually 100 per cent detection accuracy and additionally colour-highlighted the critical locations. It was also much faster than a pathologist, who took 30 hours to detect the infected samples with corresponding precision. Most notably, the clinicians overlooked metastases less than 2 millimetres in size under time pressure. Only seven of the 32 AI systems were better than the pathologists, however.
The systems involved in the test are in fact not just research projects, but are already in use. In fibrosis research at the Charité hospital in Berlin, for example, where it is using the Cognitive Workbench from a company called ExB to automate the highly complex analysis of tissue samples for the early detection of pathological changes. The Cognitive Workbench is a proprietary, cloud-based platform which enables users to create and train their own AI-capable analyses of complex unstructured and structured data sources in text and image form. Ramin Assadollahi, CEO and Founder of ExB, states: “In addition to diagnosing hepatic fibrosis, we can bring our high-quality deep-learning processes to bear in the early detection of melanoma and colorectal cancers.”
Cost savings for the healthcare system
According to PwC, AI applications in breast cancer diagnoses mean that mammography results are analysed 30 times faster than by a clinician – with an error rate of just one per cent. There are prospects for huge progress, not only in diagnostics. In a pilot study, Artificial Intelligence was able to predict with greater than 70 per cent accuracy how a patient would respond to two conventional chemotherapy procedures. In view of the prevalence of breast cancer, the PwC survey reports that the use of AI could deliver huge cost savings for the healthcare system. It estimates that over the next 10 years, cumulative savings of EUR 74 billion might be made.
Digital assistants for patients
AI is also benefiting patients in very concrete ways to overcome a range of difficulties in their everyday lives, such as visual impairment, loss of hearing or motor diseases. The “Seeing AI” app, for example, helps the visually impaired to perceive their surroundings. The app recognises objects, people, text or even cash on a photo that the user takes on his or her smartphone. The AI-based algorithm identifies the content of the image and describes it in a sentence which is read out to the user. Other examples include smart devices such as the “Emma Watch”, which intelligently compensates for the tremors typical to Parkinson’s disease patients. Microsoft developer Haiyan Zhang developed the smart watch for graphic designer Emma Lawton, who herself suffers from Parkinson’s. More Parkinson’s patients will be provided with similar models in future.