IBM has updated its artificial intelligence (AI) product, IBM Watson, giving it the ability to recognise images. Watson, which relies on cognitive learning to help it process the world in a human-like manner, can now'guess' what's happening in images fed to it via URLs. IBM has created a'Visual Recognition Demo' to showcase Watson's latest trick, which allows users to feed Watson an image before it tells you what it believes it sees. For example, supplying Watson with the image of a tiger throws up the result 77 per cent tiger, 26 per cent wild cat and 63 per cent cat. As well as identifying objects, people or animals in photos, Watson is also fairly adept at guessing what's going on in the background of images such as sunsets and other outdoor scenes.

Last month I wrote an article describing the interfaces and capabilities of Microsoft and IBM's new cloud data science products. I observed that Azure ML presents a user-friendly drag and drop data mining app for businesses, while Watson Analytics focuses on natural language queries but is still too nascent for use. A similar query for "IBM Watson Analytics" turns up 730,000 documents. Amid the deluge of coverage on both services, one could lose sight of the many upstart companies offering cloud machine learning services. However, new product categories are typically pioneered by startups.

Identifying a patient’s important medical problems requires broad and deep medical expertise, as well as significant time to gather all the relevant facts from the patient’s medical record and assess the clinical importance of the facts in reaching the final conclusion. A patient’s medical problem list is by far the most critical information that a physician uses in treatment and care of a patient. In spite of its critical role, its curation, manual or automated, has been an unmet need in clinical practice. We developed a machine learning technique in IBM Watson to automatically generate a patient’s medical problem list. The machine learning model uses lexical and medical features extracted from a patient’s record using NLP techniques. We show that the automated method achieves 70% recall and 67% precision based on the gold standard that medical experts created on a set of de-identified patient records from a major hospital system in the US. To the best of our knowledge this is the first successful machine learning/NLP method of extracting an open-ended patient’s medical problems from an Electronic Medical Record (EMR). This paper also contributes a methodology for assessing accuracy of a medical problem list generation technique.