From improved diagnosis and treatment to streamlined processes and predictive maintenance, Artificial Intelligence is bringing many benefits to the healthcare sector. Beatriz Matesanz, MIS Innovation & IrixX Business Segment Director at Thales, tells us more.
What is Artificial Intelligence bringing to the healthcare sector?
Beatriz Matesanz: One of the biggest trends in medicine today is the personalization of treatment for each patient. Artificial Intelligence is a wonderful tool to achieve this. With AI, we will be able to correlate data from different sources – such as patient history, biomarkers and imagery - in order to tailor and optimize the treatment for patients.
This could be the case for example for someone with a rare illness. Healthcare professionals can benefit from years of medical history and data from many other individuals for diagnosis and treatments, with AI algorithms trained to identify principal symptoms and successful treatments. At least two main benefits result from this; anticipation in the treatment itself as well as better prognosis for the next steps.
Globally, AI will play an increasing role in all areas of patient care, including prevention, diagnosis, treatment and prognostics.
Specifically in the medical imaging sector, how is Thales using AI?
Today, the main benefits of AI in medical imaging are the assistance this can provide through second opinion diagnosis, and the optimizing of workflow in hospitals or health centres. In order to achieve this, Thales is focusing its use of AI in two areas: optimized CAD (Computed Assisted Diagnosis) modules for our X-Ray detectors and imaging software, and predictive maintenance modules for our customer’s equipment.
Optimizing the workflow of image acquisition can begin for instance with automatic body part recognition, which will save a number of manual clicks during image acquisition. With hundreds of millions of X-Ray scans performed annually, automated identification and classification of body parts helps build incredibly useful data management tools and systems. Secondly, the CAD module helps radiologists with their diagnosis by providing a second opinion that will help them to make a final assessment on the image. The algorithm results provided - comparing patient results to an average based on a cross-section of the population for example - can serve as a simple backup check on the diagnosis. Radiologists can then dedicate the time they have saved to the more complicated cases.
Beatriz Matesanz presented the topic in this video for Expo 2020 in Dubai. The video is part of a series in the France pavillion looking at different aspects of Artificial Intelligence.
What about after sales and predictive maintenance?
The equipment we provide to customers will be available with built-in predictive maintenance algorithms and modules. This increases the availability of their medical devices by anticipating failures before they happen, ensuring the non-disruption of hospital workflows. An example would be x-ray detectors sending logs to the maintenance algorithm monitoring them and correlating with known failure patterns in order to give an exchange recommendation before the detector fails.
With the increasing use of AI in the medical sector, how can we be sure that our personal health data is secure?
First of all, data sharing between healthcare providers and AI system builders is subject to strict data protection laws. The capacity of healthcare providers to share personal patient data with an AI system builder for any purpose not connected with the direct care of a patient is therefore severely constrained.
It is important though that all players keep in mind that cyberattacks against the healthcare sector are increasing across the world. As a global player in the cybersecurity and connectivity domains, Thales pays very close attention to the security of medical data. All of the AI algorithms we use function with anonymized data. Additionally, Thales solutions - including naturally our X-Ray detectors and imaging software - are ‘cybersecured by design’, respecting medical field standards and regulations. Continuous monitoring of new threats as well as software updates ensure that patient data is always secure.