Ever imagined a technology telling you that you are diagnosed with cancer seeing your mammogram report or CT scan? Radiologists are surely needed to supervise diagnostic process; but "Artificial Intelligence" is creating its own space in the medical field. It has definitely becoming an integral part of daily routine in diagnosing simpler cases.
While automation enhances and improves the role of information specialists, there is still a need for the human perspective.
Artificial intelligence which is the imitating human understanding by computers is now finally a reality in the medical field. Combining medical data with AI will change a lot of medical specialists like radiologists. Although there is worry of AI replacing such specialists, in effect, AI is a decision support technology to enhance radiologist ability to diagnose correctly.
The ability to mine the endless amounts of imaging data is driving AI innovation forward in radiology; however, as this disruptive technology and its data applications begin to find a more defined role, there are questions around its impact on the future of the industry. AI and deep learning have a wide range of applications and potential in radiology – spanning from improved diagnosis, enhanced workflow and inevitably, a shift in the radiologist's role.
AI in radiology, for example, is designed to help tease out and prepare data for the radiologist, but as it relates to evaluating scans and diagnosis, the understanding of the interaction between the imaging physics and the disease biology is better done by the radiologist.
A merging of these two data-rich fields will allow the information specialist to understand the important data and manage the information in the clinical framework of the patient to help guide clinicians. It's a natural combination in this perspective and there are three phenomena happening now driving these compelling integrations:
• Correlation and aggregation of data
• Emergence of computational framework
• The digitization of pathology
Will these "advancements" in AI force radiologists to look for new lines of work? No, it doesn't seem so. Artificial Intelligence still has multiple hurdles to clear before it sends human radiologists on their way.
As a first-of-its-kind application, as of now, there is the approval process in place to evaluate imaging AI products for compliance with FDA 510 (safety regulations). Especially in the case of radiology, the smart learning necessary to get an AI application fine-tuned and fully up-to-speed involves scanning and analyzing vast multitudes of sample images. Even if AI applications someday boast a 99 percent accuracy rate, that one percent of misread automated diagnoses may trigger a slew of potentially landscape-altering lawsuits.
Referring physicians will likely be slow to adopt computer-generated imaging diagnoses without the human element from a seasoned radiologist communication, image optimization, quality assessment and more.
Over the foreseeable years, AI and human radiologists won't become an either-or proposition. AI applications will continue to become a primary tool of computer-aided detection, a symbiosis where AI automation empowers radiologists to focus on tasks which require the human-centric intelligence computers still won't deliver – a relationship greater than the sum of its parts.