Creating a World Where Clinical Care is Data-Driven, Intelligent and Patient Focused

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This article is taken from the white paper 'Should You Be Using AI In Your Business?'. Download the complimentary paper here.

Artificial Intelligence has the capacity to transform all aspects of healthcare from the research and production of new drugs and treatments, to the way in which patients are cared for.

Cloud based company, Arterys, has created an intelligent medical imaging platform that supports advanced AI tools and ultra-fast image processing. Back in 2011 when Arterys was founded, most medical imaging happened on hospital premises with limited computing power, with measurements taken manually and automated tools minimal. Out of this, the vision to advance medical imaging via cloud computation and advanced analytics was born. 

Arterys was the first clinical cloud-based deep learning in healthcare model to receive FDA approval to be used in a clinical setting, a huge step for AI in healthcare. Their aim of creating  ‘A world where clinical care is data-driven, intelligent and patient focused’ comes to life through their ‘deep learning engine with unlimited computation to translate deep learning models into clinical products.’

PROBLEMS WITH MEDICAL IMAGING:

Medical images contain a wealth of information that isn’t being leveraged to optimize patient care. Through images, t’s difficult to precisely track disease and even more so to predict the course a disease will take or how it will respond to specific treatments. Several challenges in medical imaging are currently preventing data-driven diagnoses:


These problems deliver real-world ramifications to healthcare professionals and patients alike. The lack of efficiency means that delays and errors in screening, diagnosis and monitoring are common and lead to poor outcomes at higher costs. Delays can come from either the time it takes for an image to be interpreted, or the time it takes for physicians to detect disease progression or assess the effectiveness of a treatment. Arterys identified this issue and are leveraging their model to accelerate these insights to provide more timely treatments as well as stopping and altering treatments that do not work.

How can AI help?

Whilst the system augments the radiologist in several aspects, it does not aim to replace the human, but enhance it in the following ways:

  1. Expediting the tedious work surrounding  setup, interpretation, and reporting on image findings.
  2. Improving accuracy and consistency when measuring anatomy so the radiologist can better track changes in patients.
  3. Helping the community standardize how disease is measured and categorized, so that there is more ….consistency across practices. 

This will allow for real-world study of outcomes of similar patients and optimization of treatment for individual patients. 

Arterys have faced a good amount of skepticism surrounding AI in the clinical community, however having allowed clinicians to try the system for themselves and compare the results has proven a convincing demonstration of the success and positive impact of AI. The clinicians are then able to experience the benefits of the automation and assess its accuracy. The AI-enabled platform simultaneously enables the clinician to edit the measurements and assessments it automates, allowing them to remain in full control of the interpretation. 


What does Arterys implementation of AI teach us about cross-industry applications?

  • Costs can be cut whilst improving efficiency of data collection
  • Image analysis does not need to be time consuming, and can become more accurate
  • Jobs do not need to be lost, the employee simply becomes more effective working alongside the intelligent system
Keen to learn more about Arterys and AI in healthcare? 

Join us at the Deep Learning in Healthcare Summit in Bostol this May 24 - 25, where Daniel Golden, Head of Machine Learning at Arterys will present his most recent work. 

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Healthcare AI Deep Learning in Healthcare Summit AI Assistants MedTech


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