Category: Artificial Intelligence

15 Mar 2021

Pneumothorax Detection and Classification on Chest Radiographs using Artificial Intelligence

Pneumothorax Detection and Classification on Chest Radiographs using Artificial Intelligence

A pneumothorax is an abnormal collection of air in the pleural space between the lung and the chest wall. This air pushes on the outside of the lung, causing it to collapse. A pneumothorax can be caused by a blunt or penetrating chest injury, certain medical procedures, or from underlying lung disease, typically emphysema. Depending on its size, pneumothorax can result in complete lung collapse or collapse of only a portion of the lung. Occasionally it may occur for no obvious reason (idiopathic). Pneumothorax can potentially be life-threatening and is considered to represent a critical finding in Emergency Radiology (ER), requiring immediate reporting to the treating physician to ensure immediate medical attention. Hence, Pneumothorax detection is of critical importance in clinical care. Pneumothorax may be detected with the help of image processing and deep learning algorithms. If utilized effectively, deep learning techniques can assist radiologists with quick detection, segmentation, classification and quantification of pneumothorax. In this paper, we evaluate two deep learning architectures for the detection and segmentation of pneumothorax regions on chest radiograph images. The AI system detects regions of pneumothorax in a chest radiograph and may assist the radiologist to review on priority the cases that contain a pneumothorax and thus facilitate early management of patients.

06 Nov 2020

Enhancing RIS-PACS solution with Artificial Intelligence

It has become fairly clear of late that using technologies is aiding radiologists in not just enhancing patient care but also optimizing their time and effort. Several universities across the globe including the University of Virginia Health System are now looking for platforms that can be seamlessly integrated with Radiology Information System (RIS) and Picture Archival Communication System (PACS) to help not just streamline the workflow but also help detect findings that could be missed manually.

For instance, with the help of Artificial Intelligence (AI), loss in bone density can be found out at an early stage. There are probabilities of missing these detections while using the traditional interpretation method. Some AI Software uses colors to denote normal and abnormal findings. This speeds up the reading workflows in the PACS. Also, this is perfect for radiologists because then they know which results need his/her urgent attention. With AI software assisting in findings ranging from chest, pelvis CT scans and abdomen, coronary calcium, liver steatosis, pulmonary emphysema, spine compression fractures and bone mineral density, healthcare is all set to get transformed and how.

Preventive Care

An exciting step in preventive care, AI enhances RIS-PACS even as it leads to early detection of various conditions as well as ailments. It is but obvious that artificial intelligence is playing a key and an extremely vital role that could also go beyond regular readings and avert serious diseases from developing.

Sophisticated AI algorithms can now go hand-in-hand with RIS-PACS and deliver the added benefit of expediting the reading process while at the same time identifying findings that may go unobserved or are hard to visualize.

Enhanced Workflow

AI software can be running in the background and putting forth clinically significant and relevant findings that could have been missed.

Some software are developed with an access to in-house AI algorithms which integrates seamlessly with its RIS-PACS. This makes it convenient to easily integrate it with the workflow and give a unified AI experience to the user. These AI tools, when executed over the scanned images, empowers Radiologists to provide increased, consistently accurate and faster diagnosis.

Even in the sphere of Veterinary Sciences, AI helps with the workflow and enables integrated image management through central­ized scheduling to multiple connecting modalities, sites or centers. 

Automation Benefits

Automation is key for many AI tools and many radiologists prefer to call it the perfect assistant technology. Many AI software take on duties that make a radiologist’s job easier. When the radiologists are overburdened with studies, they tend to be in a rush and this in turn increases the error rates. AI puts together machine and humans and makes this combination much more powerful and error free.

Enhancing RIS-PACS, AI software works in tandem to lay emphasis and focus on high quality radiology reporting and accessibility. Deploying AI for radiology will only add immense value to patient diagnosis and care.

07 Feb 2019

Artificial Intelligence all set to Virtualize Radiology Brains

Mr. Prashant Akhawat’s interview with Live Mint

  • AI and automation will not replace radiologists, but will act as an assistant to radiologist in every step of the imaging detection, diagnosis and prognosis
  • There is an increase in interest and enthusiasm for AI among the radiology community as the discussion is moving upwards on from considering AI as a threat

Accelerating with an exponential growth, artificial intelligence (AI) is all set to move from experimental stages to live industry implementations and all is set to mark its presence across all industry verticals. AI is all about virtualizing human cognitive functions in the form of software brains. For organizations, harnessing AI is not optional, albeit it is critical to stay competitive. Gartner in its recent study (2018), predicts the business value derived from AI to reach $3.9 trillion by 2022. With the disruptive potential, the investments in AI are ever-increasing. It is redefining industries with automation processes, and personalization. The healthcare industry has been one of the foremost adopters of the AI amongst all others. The advancement, the healthcare industry is reaping with the use of AI to maintain medical records, do mundane tasks more accurately and faster, to design care pathways, digital consultations, medication management, drug creation, image analytics, bigdata analytics, medical robotics, health monitoring and host of other things.

Powering Radiology with AI

Today, in India, there is a growing demand for improved medical care. We see an increase in insurance penetration, the rise in chronic disease and an aging population — all this demand for better imaging diagnoses and treatment. Radiology industry is facing its own set of ongoing challenges – the shortage of radiologists topping the list. The radiology industry is a functional domain for automation by virtualizing domain intelligence into an intelligent software. The quantum leap in medical imaging technology has led to exponential growth of medical imaging data stored digitally. Deep Learning algorithms and Image Analytics can help in improving medical diagnosis and aid radiologists with better reporting efficiencies.

Nowadays, with the growing incidence of lifestyle diseases, the requirement of frequent imaging and getting multiple scans at a higher resolution has increased. There are scenarios where the hospitals have X-Rays, MG, CT and MRI equipment’s but sans radiologist to read a report. To bridge the gap where AI can play a pivotal role by performing specific tasks such as image recognition – nodule detection, hemorrhagic or ischemic stroke detection, fracture detection, breast cancer analysis with prior case analysis and other narrow tasks requisite identify potential findings in medical images, which is one set of tasks performed by radiologists. This gives a window to the radiologists to focus more on image-guided medical interventions, defining clinical parameters of imaging examinations, relating findings from images with medical records and test reports, consult physicians for treatment based on the diagnosis, discussing procedures and results with patients. Artificial Intelligence is taking over image reading and interpretation so that radiologists can read more images in a short span with better accuracy as the number of images has increased more in the last decade than the number of radiologists.

Radiologists need to adapt themselves to new skill sets and technologies for attaining better productivity by integrating AI with radiology practice. AI and automation will not replace radiologists, but will act as an assistant to radiologist in every step of the imaging detection, diagnosis and prognosis and will also help in prior analysis and comparison. AI Algorithms will have a stronghold in medical imaging and will become an integral part of RIS-PACS, as frequently we hear some or the other algorithm is developed to detect tumors, lesions, fractures and host of other things.

With the help of RIS-PACS workflow enabled with AI, a radiologist sitting at any location can do the reads for any healthcare center located remotely, which addresses the accessibility challenges, subspecialty reads, point in time care with reduced costs. Further helping them to optimize results without compromising on the accuracy of diagnosis. For emergency reporting (trauma and stroke cases) AI can assist the radiologist with a preliminary report with which they can conclude with their final interpretation of the case within the stipulated time. AI Enabled RIS-PACS can prioritize stroke or stat cases and reduces turnaround time in turn will improve patient care. The algorithms are trained to assist the radiologists with various detections, diagnosis, staging, sub classification of different medical conditions. Hence, deep learning is a shoulder to the increasing workload in radiology.

Global Market for AI

Artificial intelligence’s complexity is not a deterrent to its adoption instead it is as disruptive as the internet was. The future of investment is AI, and it has not just been observed, but also proved that AI helps in improving the radiologist’s productivity, efficiency and quality diagnosis, hence this would help them reduce costs and increase return on investments. In the healthcare industry, AI is rapidly rising in the medical imaging domain. Globally, the AI market in medical imaging is forecasted topping US$ 2 billion by 2023. Notably, there is an increase in interest and enthusiasm for AI among the radiology community as the discussion is moving upwards on from considering AI as a threat. Also, clinical applications have shown improved clinical results with the use of AI.

All the research trends underline how AI is revolutionizing radiology in the long run. AI-based companies have learned to warm-up radiologists. Realizing the technological potential of AI, radiology practitioners are partnering with AI ventures to have a seamless RIS-PACS workflow. AI is going to augment the way care is provided by healthcare practitioners.


28 Dec 2018
Dr Anjali

Artificial Intelligence in Emergency Radiology

A Report, based on the Keynote Speech delivered
– By –
Dr. Anjali Agrawal, Head, Teleradiology Solutions, Delhi Operations
@ Artificial Intelligence in Radiology 2018 Symposium, November 10, 2018
Organized by Telerad Tech and Image Core Lab

Dr. Anjali Agrawal addressed the audience about how AI was going to be relevant in emergency care and emergency radiology.

Deep learning and the human brain

Speaking broadly on artificial intelligence (AI), Dr Anjali said that AI is a more gen­­­eral term and includes machine learning (ML) and deep learning (DL). Machine learning, a specific type of AI, gives computers the ability to learn without being explicitly programmed. Deep learning, a subset of machine learning, mimics the human brain configuration, where the multiple neuronal layers or neurons can crunch vast amounts of data and draw conclusions. In particular, DL has immense relevance for radiology and healthcare. The availability of large amounts of annotated image datasets and increased computational power had made AI a reality and it wasn’t an illusion anymore. It is moving from experimental to the implementation phase now.

Current state of Emergency and Trauma Care

Dr. Anjali drew attention to the current state of emergency and trauma care in India. From trauma registry, it was a documented fact that trauma related deaths in India occurred every 1.9 minutes. The mortality in serious injuries was 6 times worse in a developing country such as India, as compared to a developed country. A WHO survey revealed that there were more deaths due to lack of timely care than due to other diseases like AIDS, Malaria and TB, all put together.

Dr. Anjali said that, more than 80% of Indians didn’t get care within the golden hour and she highlighted the challenges in emergency care and radiology. There was tremendous pressure on the limited resources that were available at one’s disposal. The process from the scene of accident to the emergency room is disorganized. Dr. Anjali drew attention to the education system and training, which was quite heterogeneous.

AI will transform the ER services

As per Dr. Anjali, AI could be very useful for triage in the emergency room. Studies have shown that Emergency Severity Index assignment by doctors and nurses is correct only 60% of the time. They ended up under-triaging almost 27% of the patients. And a vast majority of those, nearly half, went into the mid-acuity group, level 3 – a typical human tendency to play safe.

AI and deep learning could help by analyzing the complex data from various sources – the age and sex of the patient, presenting history, complaints, vital signs, what was the mode of transport – did the patient walk-in or was s/he brought by an ambulance, past medical history etc. This could help in transforming the emergency department operations. By using these algorithms one could accurately triage patients, so that the critically ill patients got the attention of the emergency medicine physician and were managed appropriately. These algorithms could also help in allocating resources appropriately, minimizing mismatch between staff and patient case load, with improved patient outcomes. AI could help expedite interpretation of emergent imaging studies. These algorithms would be able to make predictions of adverse events and help make an individual-specific follow-up plan.

AI will revolutionize the radiology workflow and create smart enterprises

Dr. Anjali maintained that disruptive new generation healthcare technologies such as AI, robotics, machine learning and deep learning will revolutionize radiology workflow in many ways and that it was going to lead to massive improvements in quality, value and the depth of contribution of radiology towards patient care. She stated that one of the most well researched applications is emergency radiology.

Dr. Anjali cited that, AI could help make an informed decision regarding the need for imaging and the choice of modality based on analysis of the patient records. AI enabled algorithms could play a huge role in reducing radiation doses of CT examinations or reducing scan time for MRI, by using various enhancement and post-processing techniques. However, the overall decision making and communication with the referring physician or the patient would require the intervention of a human radiologist for quite some time, despite being aided by AI.

AI can help reduce scan timings and dosages

Dr. Anjali said that, the algorithms could enhance very noisy, grainy and undersampled data, such as from MRI, which were being produced in shortened timeframes and produce high-resolution MRI images– with huge implications in the emergency room, where one tends to shy away from doing an MRI because of time constraints. For e.g., if an MRI could be done in two- thirds or one-third the time required, one would be more comfortable in sending a sick patient for a suspected hip fracture for an MRI.

Similarly, these algorithms could also be applied to Computed Tomography (CT) scanners to help reduce the CT radiation dose – a huge advantage, as the reduction in radiation dose from CT would be comparable to a standard chest x-ray. One would be able to do ultra low-dose CTs, and get more information from diagnostic quality images, compared to a radiograph.

AI can ease the workflow of a radiologist in many ways

Showcasing a typical workflow of a radiologist, Dr. Anjali said that the radiologist logs into the system, reviews his/her work list and selects a study to review. The radiologist reviews other information such as patient history, prescriptions, etc., that are related to the case. Once the images are presented, the radiologist, in most cases, adjusts the hanging protocols to enable him/her to perform the interpretation and generate a report. The initial process of arranging studies is time consuming. Citing the recent developments in reading protocols, Dr. Anjali said that it could help hang the images in an interactive manner, learning each time, catering to individual preferences, and saving time.

AI can help in detection of findings, segmentation, quantification and reporting, in a manner that is easier to understand by both the referring physicians and the patients.

Dr. Anjali maintained that AI could help the radiologist by triaging cases, such that only the positive ones could be seen by the radiologists for further interpretation. She was of the thought that, as opposed to a few articles quoting that AI would replace a radiologist for particular targeted applications,  AI would assist a radiologist, where a radiologist would act as a second reader or vice versa.

She gave one particular example where an article looked into automated detection of critical findings on non-contrast CT examinations of the head –hemorrhage, mass effect, and hydrocephalus using an AI algorithm, which would be helpful in triage. If the algorithm found the non-contrast CT to be negative, it went through another stroke algorithm. In the case of it being positive, it was labeled as a critical imaging finding, and if they were both negative, it was labelled as “no critical imaging finding”. The algorithm had a good sensitivity of 62% and a specificity of 96%, comparable to a radiologist in the detection of acute ischemia. The sensitivity and specificity were higher for detection of hemorrhage, hydrocephalus and mass effect, matching the performance of the radiologists. Therefore, the study concluded that there was a huge potential for AI algorithm in terms of screening and detection of critical findings in the emergency setting.

Dr. Anjali said that she had seen similar data in her group and had detected intracranial hemorrhage with a very high sensitivity and specificity using a hybrid approach of convoluted neural networks and factorial image analysis.  The data and the results were comparable to the existing literature and that more data pertaining to quantification and localization of intracranial hemorrhage is underway.

Juxtaposing her earlier statement on triage being the low hanging fruit for AI applications in radiology, Dr. Anjali, mentioned that apart from acute neurologic conditions, these triage tools had been used in the detection of chest radiographic findings by classifying them into normal or abnormal with a high accuracy of almost 95%.

She quoted another example of AI application – wrist fractures. These algorithms were trained by senior orthopedic surgeons. When the emergency room physicians, not trained orthopedicians or radiologists, used them, their sensitivity improved from 81% to almost 92% and specificity from 88% to 94%; with a relative reduction in misinterpretation rate of almost 47%. Dr. Anjali simplified it further by saying that the algorithm was able to emulate the diagnostic acumen of the experts by providing the labels on where the fracture was and also put a heat map, assigning a confidence level to the detected fracture.

Adding on, Dr. Anjali said that AI applications would also be useful in detection of non-acute findings the emergency radiology setting. These may be overlooked because the focus is on the critical life-threatening illnesses. AI could help with measurements of bone density, detection of fatty liver, coronary calcifications, and the presence of emphysema, which may not be relevant in the acute setting, but would have implications in the future.

AI can make expertise widely available and scalable

Dr. Anjali then stated that according to her, AI would become a huge leveler in terms of the expertise and experience of radiologists. Good radiology consult would become easily accessible, affordable, as well as scalable. In the scenario of a mass casualty incident, the algorithms could be put to use to quickly distinguish between critical and non-critical cases. Handling massive imaging volumes would also become easier.

Dr. Anjali mentioned about one particular study on automated bone age estimation where the algorithms were extremely accurate as well as reproducible, with an interpretation time of fewer than 2 seconds. This is a huge achievement because every radiologist knows how tedious and time-consuming the task of bone age determination is.

Man with machine synergy

She went to add that many similarities had been drawn between the fields of medicine and aviation. The pilot, as well as the doctor, needs to be highly skilled, as they are responsible for human lives. Both the professions have benefitted tremendously from automation. There is no flight without a human pilot, and similarly, there would be no healthcare without human doctors, because the legal responsibility would always be with the doctors. Dr. Anjali urged the doctors to not forget that medicine was an art and that the physicians needed to practice it like an art to stay relevant. AI would not replace radiologists. According to Dr. Anjali, it would be the synergy between man and machine that will help the profession as well as benefit the patients.

14 Dec 2018
Dr Arjun Kalyanpur

Artificial Intelligence in Teleradiology

A Report, based on the Keynote Speech delivered
– By –

Dr. Arjun Kalyanpur, Chief Radiologist and CEO, Teleradiology Solutions
at Artificial Intelligence in Radiology 2018 Symposium, November 10, 2018
Organized by Telerad Tech and Image Core Lab

Dr Arjun Kalyanpur began his speech recalling how Teleradiology Solutions for the past few years has been engaged in the field of artificial intelligence (AI) with the health-IT company Telerad Tech and drug trial company Image Core Lab. He also¬ congratulated Telerad Tech for launching MammoAssist – its AI software for breast cancer detection and also for establishing a new dedicated AI lab facility at the same time.

Dr. Kalyanpur noted that his company Teleradiology Solutions had reached an inflection point in its journey in the direction of artificial intelligence and it was an opportune moment for their company to be part of an event like this and be amongst the distinguished speakers in the field of AI.

AI and Teleradiology – synergistic and disruptive technologies

As per Dr. Kalyanpur, both AI and Teleradiology are very closely linked to each other and are extremely synergistic. They both address the same fundamental clinical issues about shortages of radiologists and both provide technology-enabled solutions for the same clinical issues.

Revisiting their journey of how teleradiology brought them to the current point where AI is seen as the direction forward, Dr. Kalyanpur said that the drivers that set them on this path 15 /16 years ago was the shortages of radiologists with most of them being focused in metros, and emergency radiology being the most severely affected area and still continues being one today.

He noted, that the numbers of radiologists haven’t changed much in the past 20 years and that there was still a dramatic shortage of radiologists the world over. Drawing up a scenario, Dr. Kalyanpur highlighted that even a developed country like the US with the best radiologist to population ratio, considered itself of being in the deficit with India being in the middle of the curve somewhere, whereas Tanzania on the other end had 1 radiologist for over 1 million population.

AI is important for addressing the increasing burden of diseases

He cautioned that, there is an increase in chronic diseases, due to progressively unhealthy lifestyles and living conditions, all of which required repeated imaging at multiple times in a patient’s life, leading to an increase in the number of images and their complexity in studying them. He added that in the 123 years, between the first X-Ray being produced in Roentgen’s Lab, till today, advancements have led to a dizzying array of imaging modalities of extremely high resolution, in all shades and hues being available to the radiologists, increasing work for them analyzing the data.

Understanding the gaps, Dr. Kalyanpur noted that, 15 years ago teleradiology evolved as a solution to these factors delivering the value proposition of not needing to have a radiologist at every hospital. It was technology driven solution with work flow and efficiency being at the core. It also had a quality benefit because the images were being brought to the most qualified radiologist, spawning the term “nighthawk”. The company was serendipitously at the forefront in the sector, providing night shift emergency services to hospitals at different parts of the world resulting in better performance and better quality services.

Emphasizing again, Dr. Kalyanpur said that, both teleradiology and AI were essentially disruptive technologies. Having been through and seen the evolution of teleradiology, he felt that, AI was seeing the same disruption as they had witnessed in teleradiology. Citing similarities between the two, he noted, that the resistance pattern was similar, and AI also faced the same concerns and fears that teleradiology had previously faced. Depending on how one would see them, both could either be seen as a threat or as a benefit. The same drivers were present for both innovations. Since both are IT enabled solutions and follow Moore’s law principles, one could only expect them to be faster, more effective and cheaper in the coming years. He also noted that both had the potential to transform healthcare by simultaneously increasing radiologist quality and productivity – a double whammy in terms of their benefit and impact on healthcare.

Key trends in radiology

Describing radiology as a quantitative science, Dr. Kalyanpur said that radiology today was beyond a simple image description. Studying a few trends in radiology prevailing today, Dr. Kalyanpur chose to highlight 3 of them, sub-specialization being one. Further, he noted that there was an increase in focus on report turn-around time as all of medicine was becoming metrics- driven. Turnaround time was becoming the basis of judging the performance of a radiologist or a radiology group. He also pointed out that the evolution of cloud-based workflows also had made a tremendous impact in the field.

Citing stroke as an example, Dr. Arjun said that, in the 15 years that they had been practicing teleradiology the specialty had evolved to the point where it was no longer just a matter of looking at the CAT scan of the brain and determining whether it was a stroke or not. Today they had a CT angiogram or a perfusion CT to analyze as well, increasing the number of imaging modalities available to assist in clinical decision making. Quantitative imaging today helped in quantifying the size of the stroke, determine the collateralization of the brain and the impact on patient outcome. Therefore, radiology was no longer just a diagnostic science but had evolved into being a prognostic tool while Quantitative imaging was available in every sphere – Whether it was tumor detection, tumor analysis or quantification of vascular disease.

Looking at it from the operational standpoint, Dr. Kalyanpur noted that turnaround time was another factor that entities needed to be conscious of. He mentioned that, every tele-radiologist in the organisation practiced medicine under the scrutiny of the stopwatch. Citing it as a challenge, he said that, finding a fine balance between quality and speed was important as the faster a radiologist reads an image, the greater was the chance of him/her missing a finding. As per him, teleradiology had made the important aspect of work flow possible by allowing decentralized reporting and being a cloud-based solution, a radiologist could read for hospitals in any part of the world, with the understanding that compliance with licensure and certification was adhered too.

Making a radiologist more efficient and productive

Taking a step back and speaking on his role as a radiologist, Dr. Kalyanpur said that, usually a radiologist detected and quantified the lesion, analyzed the finding, communicated and recommended the follow up. Tackling the criteria on making the radiologist more efficient and productive, he suggested making the process of reporting, the work flow smooth by providing all the relevant information to the radiologist and assisting them by creating a templated report. Speaking on the productivity, Dr. Kalyanpur said that, the average productivity of a radiologist in the world was between 6-10 RVUs (relative value unit) an hour. That could probably be increased by about 15-20% by using these techniques. However, AI could potentially double the productivity of a radiologist, thereby helping in solve the radiologist shortage issue of half the world.

Highlighting the larger issue of radiologist errors, Dr. Kalyanpur mentioned that as per some studies, 3-5% of radiologist reports contain errors, such as for example small bleeds in the brain, small clots in the lungs or very subtle fractures in the spine. Considering that a billion scans were performed around the world every year, it meant that around 30 million scans would have reporting errors – a gravely concerning huge number. He opined that radiologist accuracy and patient outcome could be improved by allowing AI to detect these findings.

Predicting on the estimates on the size of the industry, Dr. Kalyanpur said that, the initial figures in teleradiology suggested it being a billion dollar industry, however it had grown beyond 4 billion currently and he was confident that it would further grow. Similarly, he was optimistic about AI growing beyond the current 2 billion in the future and wished best of luck to Telerad Tech for its endeavours.

Developing AI in radiology

Speaking on the importance of developing AI in radiology, Dr. Kalyanpur said that, it was important to have communication between radiologists, scientists and engineers. This would enable data with labelled information help in developing the tools and would require radiologists to validate them once they were developed to confirm that they actually worked. He suggested that this could be integrated into the workflow most effectively by integrating it into the teleradiology workflow which allowed these technologies to be deployed at scale across the globe, where the entire world population of radiologists could be the consumer.

The synergies between teleradiology and AI was that at every stage of the AI cycle teleradiology had a valuable role to play and offer – whether it was data acquisition from around the world (as many as over 200 hospitals in 20 countries) forming a huge repository for analysis, testing an algorithm in a teleradiology environment and the teleradiology workflow to be used as a distribution tool for AI software.

Simplifying it, Dr. Kalyanpur noted that, teleradiology could be seen as being a shopping mall, in which each AI algorithm was an individual store or teleradiology could be an I-phone on which each individual app resided and allowed the consumer to connect with the product. He expressed his satisfaction to note that Telerad Tech, apart from developing its own software algorithm, was also partnering with other entities in the same space. And allowed their workflow to be used as a distribution module for teleradiology algorithms or for AI algorithms to radiologists around the world allowing all possibilities of detection, quantification triage for smaller lesions and detection for the larger lesions.

In his concluding remarks, Dr. Kalyanpur said that, it was all about perspective and focus. Quoting radicals such as Professor Hinton, who opined that since AI would have taken over in the future, training of radiologists should be stopped because we wouldn’t need them anymore on one hand to moderates such as Professor Langlot, Stanford, on the other who believed that radiologists who used AI will replace radiologists who wouldn’t in the future. He suggested that it would be wiser to have a pragmatic approach since it was an evolutionary area. He was of the view that one needs to learn to adapt and understand the space so that one could utilize it better. Citing Darwin he wrapped up his speech by saying that only the most responsive to change would survive over the more stronger or the more intelligent.

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