Elliot K. Fishman M.D.
Professor of Radiology, Surgery, Oncology and Urology
Johns Hopkins Hospital

Click here to view this module as a video lecture.

“Machine learning will displace much of the work of radiologists and anatomical pathologists. These physicians focus largely on interpreting digitized images, which can easily be fed directly to algorithms instead. Massive imaging data sets, com- bined with recent advances in computer vision, will drive rapid improvements in performance, and machine accuracy will soon exceed that of humans. Indeed, radiology is already partway there: algorithms can replace a second radiologist reading mammograms and will soon exceed human accuracy.”
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016

“The patient- safety movement will increasingly advocate the use of algorithms over humans — after all, algorithms need no sleep, and their vigilance is the same at 2 a.m. as at 9 a.m. Algorithms will also monitor and interpret streaming physiological data, replacing aspects of anesthesiology and criti- cal care. The time scale for these disruptions is years, not decades.”
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016

“I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon,” Hinton told me. “You’re already over the edge of the cliff, but you haven’t yet looked down. There’s no ground underneath.” Deep-learning systems for breast and heart imaging have already been developed commercially. “It’s just completely obvious that in five years deep learning is going to do better than radiologists,” he went on. “It might be ten years. I said this at a hospital. It did not go down too well.”
Geoffrey Hinton
University of Toronto

“It’s hard to predict the future, and what immensely complicates predictions over seemingly promising technologies like gene therapy or AI is how their complex construction will interface with other equally complex and dynamic technologies, all of which operate in an environment of unceasing economic and institutional flux. It remains anyone’s guess as to how AI applications will be affected by their integration with PACS, how liability trends or regulatory efforts will affect AI, whether reimbursement for AI will justify its use, how mergers and acquisitions will affect AI implementation, and how well AI models will accommodate ethical requirements related to informed consent, privacy, and patient access.”
AI Hype and Radiology: A Plea for Realism and Accuracy 
Banja J et al.
Radiology: Artificial Intelligence 2020; 2(4):e190223

”It is important to keep the evolution of the AI/ML technology in context so as not to become overly enthusiastic about the current capabilities and simultaneously not to become overly pessimistic about future developments. Though the promised delivery date of fully self-driving cars has continuously been pushed back for the past decade, it is undeniable that drivers in semiautonomous vehicles are safer than unassisted drivers. Similarly, there are tangible patient care and cost benefits to be obtained through staged development of AL/ML systems even if fully autonomous MD systems are not on the horizon.”
Applications of artificial intelligence in the emergency department 
Supratik K. Moulik, Nina Kotter, Elliot K. Fishman
Emergency Radiology (2020) 27:355–358

“The main challenge of EM involves the timely provision of medical triage defined as the process of sorting patients according to urgency and severity. This is required owing to the unpredictable nature of emergencies and conditions present where resources (e.g. staffing/beds) are sometimes limited and stretched. Relevant literature on department crowding and patient flow have shown impacts to quality of patient care. To a grimmer extent, such instances are also linked to increased mortality levels. The use of machine learning and deep learning can potentially help discern patterns of data gathered over the years and shed insights for improvement in ED processes.”
Artificial Intelligence and Machine Learning in Emergency Medicine 
Tang KJW et al.
Biocybernetics and Biomedical Engineering 41 (2021) 156–172

“These four main applications are: (1) Pre-hospital emergency management, (2) Patient acuity, triage and disposition, (3) Prediction of medical ailments and conditions, and (4) Emergency department management.” 
Artificial Intelligence and Machine Learning in Emergency Medicine 
Tang KJW et al.
Biocybernetics and Biomedical Engineering 41 (2021) 156–172

“This article aims to offer an evidence-based discourse about the evolving role of artificial intelligence in assisting the imaging pathway in an emergency and trauma radiology department. We hope to generate a multidisciplinary discourse that addresses the technical processes, the challenges in the labour-intensive process of training, validation and testing of an algorithm, the need for emphasis on ethics, and how an emergency radiologist’s role is pivotal in the execution of AI-guided systems within the context of an emergency and trauma radiology department. This exploratory narrative serves the present-day health leadership’s information needs by proposing an AI supported and radiologist centered framework depicting the work flow within a department.”
Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department 
Sabeena Jalal et al.
Canadian Association of Radiologists’ Journal 2021, Vol. 72(1) 167-174

• order entry for imaging studies optimized by AI (Step # 1)
• image protocolling streamlined by AI (Step # 2)
• image acquisition supported by AI (Step # 3)
• image post-processing supported by AI (Step # 4)
• decision support provided by AI (Step # 5)
• clinical decision support provided by AI integration (Step # 6)

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department 
Sabeena Jalal et al.
Canadian Association of Radiologists’ Journal 2021, Vol. 72(1) 167-174

Optimal scan protocols can be done with decreased interaction on a case by case basis with the radiologist and radiologic technologist

Approval of artificial intelligence and machine learning based medical devices in the USA and Europe (2015–20): a comparative analysis
Urs J Muehlematter, Paola Daniore, Kerstin N Vokinger
Lancet Digit Health 2021; 3: e195–203

’The FDA has taken an aggressive stance on AI and has published several articles with guidelines on what they expect developers of AI software to provide to them and under what circumstances they will grant application approval. In the short term, it appears that the FDA is taking a mostly middle-of-the-road path where they are approving apps that triage workflow but do not change the basic role of the radiologist in reading the study. Whether the approval is for wrist fractures, pulmonary embolism, intracranial bleed, or pneumothorax, the apps simply change the places of studies in the queue but do not alter the number of cases radiologists will eventually read.’
The first use of artificial intelligence (AI) in the ER: triage not diagnosis 
Edmund M. Weisberg, Linda C. Chu, Elliot K. Fishman
Emergency Radiology (2020) 27:361–366

“The FDA strategy of approving programs that do not change the radiologist’s final read of a study protects the patient and provides a comfort zone for radiologists. Various articles have suggested that radiologists are concerned about the potential impact of AI on the job market. Many junior people, including medical students and residents, are clearly worried that the use of AI may reduce employment or yield decreased reimbursement. While the future is impossible to predict, AI is unlikely to exert such impacts in the short term.”
The first use of artificial intelligence (AI) in the ER: triage not diagnosis 
Edmund M. Weisberg, Linda C. Chu, Elliot K. Fishman
Emergency Radiology (2020) 27:361–366

”As we have maintained in another venue, though, radiologists will continue to be crucial in rendering complex ideas intelligible and interpreting the results of advancing technologies such as AI and machine learning. The first wave of AI applications is not replacing radiologists. Rather, the innovative software is improving throughput, contributing to the timeliness in which radiologists can get to read abnormal scans, and possibly enhances radiologists’ accuracy. As for what the future brings, time will tell.”
The first use of artificial intelligence (AI) in the ER: triage not diagnosis 
Edmund M. Weisberg, Linda C. Chu, Elliot K. Fishman
Emergency Radiology (2020) 27:361–366

“The FDA approval process to date has focused on applications (apps) that affect patient triage and not necessarily apps that have the computer serve as the only or final reader. We have chosen a select group of apps to provide the reader with a sense of the current state of AI use in the ER setting. Because adoption of new technology and FDA approval are always works in progress, it is not our intention here to be comprehensive. For a more thorough review of approved AI applications, please see the American College of Radiology record available here (https:// www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms).” 
The first use of artificial intelligence (AI) in the ER: triage not diagnosis 
Edmund M. Weisberg, Linda C. Chu, Elliot K. Fishman
Emergency Radiology (2020) 27:361–366

The OsteoDetect software is a computer-aided detection and diagnostic software that uses an artificial intelligence algorithm to analyze two-dimensional X-ray images for signs of distal radius fracture, a common type of wrist fracture. The software marks the location of the fracture on the image to aid the provider in detection and diagnosis.

OsteoDetect analyzes wrist radiographs using machine learning techniques to identify and highlight regions of distal radius fracture during the review of posterior-anterior (front and back) and medial-lateral (sides) X-ray images of adult wrists. OsteoDetect is intended to be used by clinicians in various settings, including primary care, emergency medicine, urgent care and specialty care, such as orthopedics. It is an adjunct tool and is not intended to replace a clinician’s review of the radiograph or his or her clinical judgment.

“BriefCase uses an artificial intelligence algorithm to analyze images and highlight cases with detected ICH on a standalone desktop application in parallel to the ongoing standard of care image interpretation. The user is presented with notifications for cases with suspected ICH findings. Notifications include compressed preview images that are meant for informational purposes only and not intended for diagnostic use beyond notification. The device does not alter the original medical image and is not intended to be used as a diagnostic device."

Our group evaluated the performance of a convolutional neural network (CNN) model developed by Aidoc (Tel Aviv, Israel). This model is one of the first artificial intelligence devices to receive FDA clearance for enabling radiologists to triage patients after scan acquisition. The algorithm was tested on 7112 non-contrast head CTs acquired during 2016–2017 from a two, large urban academic and trauma centers. Ground truth labels were assigned to the test data per PACS query and prior reports by expert neuroradiologists. No scans from these two hospitals had been used during the algorithm training process and Aidoc staff were at all times blinded to the ground truth labels. Model output was reviewed by three radiologists and manual error analysis performed on discordant findings. Specificity was 99%, sensitivity was 95%, and overall accuracy was 98%.”
The Utility of Deep Learning: Evaluation of a Convolutional Neural Network for Detection of Intracranial Bleeds on Non-Contrast Head Computed Tomography Studies 
Ojeda P, Zawaideh M et al.
Medical Imaging 2019: Image Processing, edited by Elsa D. Angelini, Bennett A. Landman, Proc. of SPIE Vol. 10949, 109493J

“Specificity was 99%, sensitivity was 95%, and overall accuracy was 98%.”
The Utility of Deep Learning: Evaluation of a Convolutional Neural Network for Detection of Intracranial Bleeds on Non-Contrast Head Computed Tomography Studies 
Ojeda P, Zawaideh M et al.
Medical Imaging 2019: Image Processing, edited by Elsa D. Angelini, Bennett A. Landman, Proc. of SPIE Vol. 10949, 109493J

Conclusions: TAT was reduced in the month following AI implementation among all categories of head CT. Overall, there was a 24.5% reduction in TAT and a slightly greater reduction of 37.8% in all ICH-positive cases, suggesting the positive cases were prioritized. This effect extended to ED and inpatient studies. The reduction in overall TAT may reflect the disproportionate inlfuence positive cases had on overall TAT, or that the AI widget also flagged some cases which were not positive, resulting in the prioritization of all NCCT over other exams. 
Comparison of After-Hours Head CT Report Turnaround Time Before and After Implementation of an Articial Intelligence Widget Developed to Detect Intracranial Hemorrhage. 
Brady Laughlin et al.
American College of Radiology(2018)

courtesy of AIDoc

“We trained a fully convolutional neural network with 4,396 head CT scans performed at the University of California at San Francisco and affiliated hospitals and compared the algorithm’s performance to that of 4 American Board of Radiology (ABR) certified radiologists on an independent test set of 200 randomly selected head CT scans. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 ± 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists.” 
Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning 
Kuo W et al.
Proc Natl Acad Sci U S A. 2019 Nov 5;116(45):22737-22745