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AI-Pathway companion prostate cancer from Siemens Healthineers approved for use in Europe as medical device

Press releases may be edited for formatting or style | March 04, 2020 Artificial Intelligence Health IT

AI-Pathway Companion Prostate Cancer helps match the data available for the individual patient with the guidelines to identify the recommended treatment approach and facilitate the appropriate disease management. The digital companion searches the patient record and other sources, like the hospital information system or PACS (Picture Archiving and Communication System), and compiles the longitudinal data for the cancer patient in question.4 Natural Language Processing is used to extract and compile data relevant to the decision-making process from the radiology, pathology, genetics, and lab results, and present it via an intuitive user interface. The PI-RADS score is also automatically correlated with the Gleason score to help physicians estimate the aggressiveness of the tumor and determine the course of the disease on that basis. Algorithms search through the prostate cancer guideline for recommendations that suit the patient’s individual disease status based on his or her current available data. The algorithms automatically show where the patient is in the pathway and recommend next options, including any missing information that is required.8

Based on this data, AI-Pathway Companion Prostate Cancer displays the patient’s current clinical situation and offers guideline-based recommendations for further steps to provide treatment in accordance with the medical evidence. The digital companion can thus help multidisciplinary teams at tumor boards, for example, to make optimized decisions throughout the treatment process.

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“Our multidisciplinary team (MDT) discussions can greatly vary in time. If a clinical decision support solution could integrate and display the patient context in a smart and standardized way, while providing evidence-based diagnosis and therapy recommendations, it could help make the discussions shorter and save time for all the MDT participants,” says Prof Helge Seifert, MD Chairman, Clinic for Urology at University Hospital Basel.

I spend a lot of time entering patient information manually in our MDT solution. If this information could be integrated automatically and in a smart and standardized way, it would save us a lot of time and let us focus on what's important: the patient,“ says Christian Wetterauer, MD Senior Urologist at University Hospital Basel.10

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