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.
Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings. That said, deep learning is rapidly advancing, and it's a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology.
Futurists sometimes claim that artificial intelligence (AI) will make radiologists obsolete. Their argument has been that compared to humans, algorithms are better and faster at analyzing medical images such as X-rays. So why has this hype failed to become reality? In this opinion piece, Ulysses Isidro and Saurabh Jha write, "For radiology AI to be widely adopted, it needs to overcome several barriers. Most of all, it needs to make the radiologist's work simpler."
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.
Paging HAL: What Will Happen When Artificial Intelligence Comes to Radiology? The myth of Hephaestus' golden handmaidens illustrates mankind's centuries-long fascination with artificial intelligence (AI). The god of the forge created his handmaidens, who could talk and perform even the most difficult tasks, to assist him in his labors, and many people have since speculated about the possible uses of AI and the forms it might take. More recently, noted scientists and futurists, such as Ray Kurzweil; Stephen Hawking, CH, CBE, FRS, FRSA; and Elon Musk, have discussed, debated, and dissected the possibilities and pitfalls of AI. With many AI advances coming in the past few years, some people are beginning to wonder whether it will eventually replace radiologists.