Recent quotes:

Opinion: Just what the doctor ordered: How AI will change medicine in the 2020s - The Globe and Mail

For decades, there has been a steady erosion of the practice of medicine, with progressively less time between patients and doctors, a global epidemic of physician burnout that has now reached a crisis, a doubling of medical errors when doctors have symptoms of depression and most serious errors attributable to bad clinical judgment. Concurrently, each patient’s cumulative data, such as prior history, laboratory tests, scans and sensor output, keeps growing, as has the doctor’s relegation to the role of data clerk. The limited time to think has led one leading physician to conclude: “Modern medical practice is a Petri dish for medical error, patient harm and physician burnout."

Announcing ICD-10-CM and RxNorm Ontology Linking for Amazon Comprehend Medical

Medical ontologies, such as ICD-10, make it possible to classify unstructured medical information into standardized codes that downstream healthcare applications, such as revenue cycle management tools (medical coding) can read. Amazon Comprehend Medical ICD-10-CM RXNorm Ontology Linking extracts medical condition and medication entities from medical text and links them to the relevant ICD-10-CM and RXNorm concepts respectively.   Using Amazon Comprehend Medical ICD-10-CM and RXNorm Ontology Linking APIs, developers can quickly and accurately extract codes (e.g. “R51” as the ICD-10-CM code for headache) from a variety of data sources, such as doctor’s notes or patient health records. Our deep learning approach to ontology linking provides much higher accuracy than existing rules-based systems by understanding the context each entity is found in.

Real-time personalization and recommendation | Amazon Personalize | AWS

Delivering personalization to individuals at scale requires a combination of the right data and the right technology. The algorithms used by Amazon Personalize are designed to overcome common problems when creating custom recommendations – such as new users with no data, popularity biases, and evolving intent of users – to deliver high-quality recommendations that respond to specific needs, preferences, and behavior of your users.