AI’s promise for medical imaging and early detection of chronic conditions

Patient care still lies in the hands of healthcare practitioners, but now via AI solutions, they can be empowered to fight the growing burden of chronic conditions, more easily act on incidental findings, and most importantly, improve patient outcomes.

Americans struggle with chronic disease, with six out of 10 living with a chronic disease, and four out of 10 living with two or more.

We can expect to continue seeing the immeasurable burden chronic diseases place on our aging population and healthcare system.

Over time, medicine has advanced and evolved to meet situational demands as innovative technologies become available. For example, not long ago, imaging specialists were reading and storing physical copies of scans. Today, they can access and analyze images with the click of a button on their computer. AI should be seen as a new technical solution for addressing the rise of chronic diseases, by helping screen for early signs of conditions like heart disease, osteoporosis and diabetes. With the advent of new AI and machine learning technologies, there is an opportunity to enhance medical imaging and provide significant relief to burdened hospitals.

Using the massive amounts of imaging data already available to the healthcare system, AI can help highlight early, previously undetected signs of common chronic diseases.

In cardiology, for example, coronary artery calcium (CAC) is regarded as a vital biomarker for the detection of cardiovascular disease (CVD), the leading cause of death around the world. While chest CT (Computed Tomography) scans are commonplace, assessing a patient’s CAC levels can be a cumbersome, time-consuming manual task. AI algorithms can be incorporated into existing imaging workflows to help identify and quantify CAC levels and help physicians determine a patient's risk of CVD and the potential need for further analysis and a treatment plan.

AI software, such as Nanox AI solutions, can also provide a deeper analysis of the spine from medical images and automatically identify findings suggestive of compression fractures and low bone mineral density, enabling further work up and treatment of patients with osteoporosis to prevent potentially life changing major osteoporotic fractures, such as a hip fracture. This clinical decision assist tool could be life-changing and even lifesaving, as hip fractures alone account for more disability-adjusted life-years lost than breast cancer, prostate cancer, arthritis, and Parkinson’s disease combined.

AI solutions like these can automatically recognize complex patterns in imaging data and provide the necessary quantitative, rather than qualitative, assessments of medical images. Utilizing AI in this way to uncover subtle indications of chronic conditions across the board can initiate further medical assessment to establish preventative care pathways for patients, which is particularly important as the U.S. healthcare system is moving toward value-based care (VBC). With a looming shortage of practicing cardiologists and radiologists, there’s concern about the ability to meet the growing demand of patients needing imaging care. According to the Association of American Medical Colleges (AAMC), 60 percent of all cardiologists are over 55, and by 2030, we could be short by 120,000. AAMC also found that the US is projected to have a shortage of 17,000 to 42,000 radiologists and other clinical specialists by 2033.

An additional challenge as technology advances and digital imaging improves is that imaging specialists are often faced with an increased number of images per scan, prolonging their valuable time on each scan and/or the additional data is left without being utilized.

These factors place an urgency on freeing radiologists from the burden of tedious and repetitive tasks, so that they can focus on the true goal of their practice: caring for patients. AI can help make this a reality. By shifting the burden of increased manual work from radiologist to machine, AI allows them to apply their time and energy to better serving more patients. This is a clear path toward VBC and optimizing patient care. By maximizing and utilizing data, AI and machine learning can simultaneously reduce costs while assisting healthcare practitioners in early detection, prevention and slowing the progression of chronic medical conditions.

For example, Nanox AI enables more accurate risk-adjustment so patients are provided with preventative care paths they need, while improving quality of health services at significantly lower associated costs. Providing a fast and efficient supporting diagnosis, AI can reduce reporting times, improve analysis, automate basic tasks, and integrate seamlessly with workflows, potentially reducing the overload crisis to manageable levels.

While AI will never be able to replace healthcare practitioners, it is a tool that can help them facilitate in depth analysis for chronic conditions, act on incidental findings and increase efficiency. Patient care still lies in the hands of these imaging specialists, but now via AI solutions, they can be empowered by advancements in technology that understand their unique situational needs. This helps our healthcare system fight the growing burden of chronic conditions, and, most importantly, improve patient outcomes.

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