| | August 20189In the long-term, one is looking at development of higher cognitive functions impacting complex decision making, such as combining the interdisciplinary data with the imaging data to arrive at the most likely diagnosisradiologist. For instance, tumor quantification for treat-ment initiation & response is a tedious job with a human manually segmenting the tumor on each cross-sectional image, which could add up to a hundred. This task may be successfully performed by automated segmenta-tion tools. Various parameters beyond gross anatomical features and not entirely discernible to the human eye, such as shape, pixel density/intensity, texture analysis of a lesion may be assessed by `intelligent machines'. The data emanating from the field of genomics and vari-ous quantitative parameters from the digital images are propelling the specialty into new fields of collaborative diagnostics, such as radiogenomics & radiomics, mak-ing diagnostics more precise & personalized. Unlike, the human eye, computer vision has no established prefer-ences for data display, and can get the job done without any distortion or dilution of details, that an image display system may entail.These changing paradigms in healthcare delivery certainly challenge the traditional approaches to diagnostics. The immediate benefits are being reaped in improving the workflow. There is a huge shortage of radiologists worldwide and more so, in India. The training & skill refinement cycle of a radiologist is long compared to the actual productive years. And then, detection & interpretation skills are not equal. AI tools would help radiologists become more accurate by providing a `second read,' more efficient by making accurate lesion detections & quantifications, and more productive overall. Computer vision can help triage cases on the PACS worklist to ensure that the most critical get the most immediate attention. Busy practices, such as ours, are in the process of integrating AI tools with PACS, so that a preliminary report is generated before even the radiologist evaluates the study. Based on the automated detection, the cases are prioritized on the worklist. A critical case like a head CT scan with bleed in the brain is flagged at the top and normal ones to the bottom. Thus, computer vision algorithms will have a major role in Intelligence Augmentation, by acting as lesion finders, and formulating a list of differential diagnoses. In the long-term, the horizon of which is anyone's guess, one is looking at development of higher cognitive functions impacting complex decision making, such as combining the interdisciplinary data with the imaging data to arrive at the most likely diagnosis, with recommendations for further work-up and prognosis.Increasing automation of processes is good for all. Increased machine cognition would minimize the divide between the experienced and the not so, culminating in a more standardized patient care. However, a conceivably difficult but not entirely impossible outcome may also be displacement of the traditional radiologist or pathologist in the healthcare delivery chain and disruption of conven-tional practices & workforces. A new field of healthcare diagnostics may emerge with informatics, computer vi-sion & AI training being an essential part of training and practice. The need to develop a synergy with the machines would no longer be optional, but critical to stay relevant. Soft skills such as effective communications, ability to lead and collaborate, in addition to domain knowledge, would be defining factors of a good diagnostician. Dr. Anjali Agrawal
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