Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline. However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology. There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology. The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays.
Machine learning was undoubtedly one of the hottest topics in radiology last year, with a steady stream of academic research papers highlighting how machine learning, particularly deep learning, can outperform traditional algorithms or manual processes in certain use-cases. Investment in machine learning start-ups also continued, with several companies attracting early stage funding. To date, more than $100m has been invested in start-ups that are developing AI solutions for radiology. Furthermore, commercial activity gained pace, with at least 20 companies exhibiting AI-based products at the RSNA conference towards the end of the year, although most were prototypes and only a handful had regulatory clearance. Whilst the enthusiasm for machine learning is certainly justified, it inevitably raises expectations, potentially to unrealistic levels.
Deep learning, also known as artificial intelligence, will increasingly be used in the interpretation of medical images to address many long-standing industry challenges. This will lead to a $300 million market by 2021, according to a new report by Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare information technology industry. In most countries, there are not enough radiologists to meet the ever-increasing demand for medical imaging. Consequently, many radiologists are working at full capacity. The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field.
Matthew Michela, president and CEO of Newton, Mass.-based Life Image, says there are three questions healthcare executives need to ask when assessing the value of an artificial intelligence product for radiology. Life Image provides access to points-of-care and curated clinical and imaging data. According to the company, it delivers large-scale, heterogenous, de-identified imaging sets that are linkable to other longitudinal data. What follows are the three questions Michela says healthcare leaders must ask about artificial intelligence products for radiology. Question 1: Does the Product Solve a Relevant Clinical Problem?
Burnout has become a popular buzzword in today's business world, meant to describe prolonged periods of stress in the workplace leading to feelings of depression and dissatisfaction with one's occupation. The topic has become so pervasive that the World Health Organization (WHO) addressed it at its 2019 World Health Assembly in Geneva in May, adding burnout to the 11th revision of the International Classification of Diseases (ICD-11) -- although classifying it as an "occupational phenomenon" rather than a medical condition. Healthcare itself is not immune to burnout, and a recent study in Journal of the American College of Radiology demonstrates it is taking a toll on pediatric radiologists in particular. The study surveyed Society of Pediatric Radiology (SPR) members and found nearly two-thirds expressed at least one symptom of burnout. While burnout is a complicated phenomenon and no two people experience it the same way, a commentary on the study suggests artificial intelligence (AI) could help alleviate some of the difficulties that can lead to burnout.