Imaging Pearls ❯ Deep Learning ❯ AI and the Emergency Room
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- “Integrating artificial intelligence (AI) in emergency radiology, particularly in the context of WB-CTbscans for polytraumatized patients, represents a substantial advancement in the diagnosis and subsequent management of traumatic injuries. A review of the current literature underscores the promising potential of AI to enhance the accuracy, efficiency, and predictive capabilities of radiological assessments in the emergency and trauma settings. Zhou and colleagues conducted a study on 133 patients comparing the results of rib fracture detection using initial CT and follow-up CT, revealing that AI-assisted diagnosis statistically significantly improved the overall accuracy for the detection of rib fractures.”
Trauma and ‘Whole’ Body Computed Tomography Role, Protocols, Appropriateness, and Evidence to Support its Use and When
Daniela Galan et al.
Radiol Clin N Am 62 (2024) 1063–1076 - “The innovative application of AI extends beyond these examples. AI has been employed in the quantitative analysis of traumatic hemoperitoneum,70 highlighting its ability to aid in diagnosing and managing intrabdominal bleeding. Seyam and colleagues implemented an AI-based detection tool for intracranial hemorrhage (ICH) and evaluated its diagnostic performance, assessing clinical workflow metrics compared to pre-AI implementation.71 The study found that the tool demonstrated practical diagnostic performance with an overall accuracy of 93.0%, sensitivity of 87.2%, and a negative predictive value of 97.8%. However, it identified lower detection rates for specific subtypes of ICH, such as 69.2% for subdural hemorrhage and 77.4% for acute subarachnoid hemorrhage. It is important to define a clear framework for clinical integration, recognizing the limitations of AI.”
Trauma and ‘Whole’ Body Computed Tomography Role, Protocols, Appropriateness, and Evidence to Support its Use and When
Daniela Galan et al.
Radiol Clin N Am 62 (2024) 1063–1076
- Purpose There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/ machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members.
Conclusion ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.
A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations
Anjali Agrawal et al.
Emergency Radiology (2023) 30:267–277 - “Just over half of respondents among the ASER membership currently use commercial AI tools in their practice. Two thirds of respondents who currently use AI tools feel that they improve quality of care, and most find themselves disagreeing with AI predictions in 5–20% of studies. Concerns and apprehensions pertaining to overdiagnosis and generalization to their local patient populations are shared by over half of end-users. The majority of respondents expect to see transparent and explainable AI tools with the onus of the final decision with the radiologist.”
A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations
Anjali Agrawal et al.
Emergency Radiology (2023) 30:267–277 - Purpose To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.
Conclusions Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
David Dreizin et al.
Emergency Radiology (2023) 30:251–265 - “Approximately 84% of studies described siloed datasets with fewer than 5000 patients. Cross-sectional imaging datasets for abdominopelvic and chest trauma ranged from fewer than 100 to 778 patients, and no commercial products were described in these domains. Torso pathology including organ injury, contusion, and hemorrhage is highly variable in size and appearance with small target to volume ratios, and multiscale DL-based tools for torso pathology have been late-comers and were not reported for trauma until 2020.”
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
David Dreizin et al.
Emergency Radiology (2023) 30:251–265 - “Even though trauma remains the leading cause of death and disability in patients under 45 years of age, trauma imaging remains a relatively small and underfunded branch of radiology. In the field of radiology as a whole, AI/ML publications have increased exponentially, primarily in the fields of neuroradiology, abdominal imaging, and chest imaging, spurred by federal agency and industry-side funding. Our findings suggest that increased funding opportunities, researcher engagement, research training, and institutional buy-in will accelerate research productivity and translation of tools to the trauma setting.”
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
David Dreizin et al.
Emergency Radiology (2023) 30:251–265 - ”In conclusion, AI CAD tools are likely to improve ER/ trauma radiologist productivity and diagnostic performance, reduce turnaround times, decrease ER and hospital stays, and improve survival of severely injured patients. However, these tools are currently in a very early stage of maturity. There are few FDA-approved products for a limited number of use cases, and there has not been sufficient validation of commercial tools to generate meta-analyses. The scarcity of large heterogeneous datasets with high-quality annotation continues to pose a major barrier. There remains an unmet need for out-of-the-box tools that accelerate data labeling and for multicenter privacy-preserving distributed learning.”
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
David Dreizin et al.
Emergency Radiology (2023) 30:251–265 - “A greater emphasis should be placed on performance validation data that incorporates assessment of bias and robustness across relevant subgroups. The methodology used for ground truth annotation is highly variable across the body of literature in this area. Researchers should be encouraged to employ independent readers with arbitration and provide data on reader agreement and reproducibility of measurements. Additionally, the range of techniques for explainability and interpretability using scalable DL-based approaches remains narrow, and methods that build trust through human–computer interaction are lacking. More emphasis should be placed on evaluation of end-user assessment of system benevolence and capability. Finally, an increase in funding opportunities would likely accelerate the R&D pipeline for trauma imaging CAD tools.”
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
David Dreizin et al.
Emergency Radiology (2023) 30:251–265
- “Our recent research, which is going to be published in Management Science, identifies a much better approach that would be relatively easy to implement. Using the same relative benchmarking idea that is currently used to incentivize cost reduction, hospitals’ waiting times should be measured (as they are) and benchmarked against the national (risk-adjusted) average waiting time of patients with similar conditions. Hospitals that exhibit shorter waiting times than the average should be financially rewarded, while underperforming hospitals should be penalized.”
To Reduce Emergency Room Wait Times, Tie Them to Payments
Nicos Savva and Tolga Tezcan
HBR February 06, 2019 - “Our research shows that such financial and outcomes-based incentives create indirect competition on waiting times and have the same effect on outcomes as direct competition has on other service points, without patients needing to exercise choice. This solution would work without requiring the regulator to figure out the thorny question: What is an acceptable waiting time and how much would it cost?”
To Reduce Emergency Room Wait Times, Tie Them to Payments
Nicos Savva and Tolga Tezcan
HBR February 06, 2019 
- AI is perhaps ideal for the ER where the diagnostic process is compressed in time and often location
- 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 - Step 1: order entry for imaging studies optimized by AI
- Protocols designed for clinical presentation (i.e. stroke, trauma)
- Protocols designed based on evaluation in the ambulance (i.e. acute abdomen, possible dissection
- Optimal use of imaging resources (and preventing overuse) - Step 2: image protocolling streamlined by AI
- Selecting the right study (CT vs MR vs US) as well as designing the right protocol is critical
- Errors in designing ER protocols for CT can result in suboptimal studies (i.e. wrong phase/phases of acquisition - Step 3: image acquisition supported by AI
- Optimal scan protocols can be done with decreased interaction on a case by case basis with the radiologist and radiologic technologist - Step 4: image post-processing supported by AI
- add 3D especially cinematic rendering for the radiologist and referring clinician and the image generation is done with AI
- Additional post processing including volume calculations can be done with AI support - Step 5: decision support provided by AI
- Triage and work list optimization
- AI assisted detection of pneumothorax or pneumoperitoneum and spinal fractures
- AI detection of incidental findings and management of these findings - Step 6: clinical decision support provided by AI integration
- AI assisted clinical diagnosis and management decisions including recent work on sepsis
- The rle of wearable devices in the ER may help with monitoring patients especially when they go to Radiology for exams. - Challenges to AI in the ER
- Defining the problem in finite demensions to allow for product development
- Funding and resources in the current environment
- Availability of computing resources needed (its all about the people)
- Multidisciplinary challenges and turf battle issues - “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 - “The use of AI appears to show significant promise in ER triage in the present. We briefly discuss the emerging effectiveness of AI in the ER imaging setting by looking at some of the products approved by the FDA and finding their way into “practice.” The FDA approval process to date has focused on applications that affect patient triage and not necessarily ones that have the computer serve as the only or final reader. We describe a select group of applications to provide the reader with a sense of the current state of AI use in the ER setting to assess neurologic, pulmonary, and musculoskeletal trauma indications. In the process, we highlight the benefits of triage staging using AI, such as accelerating diagnosis and optimizing workflow, with few downsides.”
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 - Triage in the ER: AI Apps with FDA Approval
- Intracranial bleed
- Pulmonary embolism detection
- Pneumothorax detection
- Skeletal trauma - "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 radi- ologist 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 - Background: Radiology reporting of emergency whole-body computed tomography (CT) scans is time- critical and therefore involves a significant risk of pathology under-detection. We hypothesize a relevant number of initially missed secondary thoracic findings that would have been detected by an artificial intelligence (AI) software platform including several pathology-specific AI algorithms.
Methods: This retrospective proof-of-concept-study consecutively included 105 shock-room whole-body CT scans. Image data was analyzed by platform-bundled AI-algorithms, findings were reviewed by radiology experts and compared with the original radiologist’s reports. We focused on secondary thoracic findings, such as cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms and vertebral fractures.
Results: We identified a relevant number of initially missed findings, with their quantification based on 105 analyzed CT scans as follows: up to 25 patients (23.8%) with cardiomegaly or borderline heart size, 17 patients (16.2%) with coronary plaques, 34 patients (32.4%) with aortic ectasia, 2 patients (1.9%) with lung lesions classified as “recommended to control” and 13 initially missed vertebral fractures (two with an acute traumatic origin). A high number of false positive or non-relevant AI-based findings remain problematic especially regarding lung lesions and vertebral fractures.
Conclusions: We consider AI to be a promising approach to reduce the number of missed findings in clinical settings with a necessary time-critical radiological reporting. Nevertheless, algorithm improvement is necessary focusing on a reduction of “false positive” findings and on algorithm features assessing the finding relevance, e.g., fracture age or lung lesion malignancy.
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - Background: Radiology reporting of emergency whole-body computed tomography (CT) scans is time- critical and therefore involves a significant risk of pathology under-detection. We hypothesize a relevant number of initially missed secondary thoracic findings that would have been detected by an artificial intelligence (AI) software platform including several pathology-specific AI algorithms.
Methods: This retrospective proof-of-concept-study consecutively included 105 shock-room whole-body CT scans. Image data was analyzed by platform-bundled AI-algorithms, findings were reviewed by radiology experts and compared with the original radiologist’s reports. We focused on secondary thoracic findings, such as cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms and vertebral fractures.
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - Results: We identified a relevant number of initially missed findings, with their quantification based on 105 analyzed CT scans as follows: up to 25 patients (23.8%) with cardiomegaly or borderline heart size, 17 patients (16.2%) with coronary plaques, 34 patients (32.4%) with aortic ectasia, 2 patients (1.9%) with lung lesions classified as “recommended to control” and 13 initially missed vertebral fractures (two with an acute traumatic origin). A high number of false positive or non-relevant AI-based findings remain problematic especially regarding lung lesions and vertebral fractures. Conclusions: We consider AI to be a promising approach to reduce the number of missed findings in clinical settings with a necessary time-critical radiological reporting. Nevertheless, algorithm improvement is necessary focusing on a reduction of “false positive” findings and on algorithm features assessing the finding relevance, e.g., fracture age or lung lesion malignancy.
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - “Based on 105 “shock-room” emergency CT scans, we demonstrated an AI system that would have decreased the number of missed secondary thoracic findings in an AI- assisted reading setting. The added clinical value could be quantified by the number of additional findings as follows: up to 25 (23.8%) patients with cardiomegaly or borderline heart size, 17 (16.2%) patients with coronary plaques, 34 (32.4%) patients with dilatations of the thoracic aorta, 13 additional vertebral fractures (two of them with an acute traumatic origin) and three lung lesions of two different patients that were radiologically classified as “in recommendation to control”.”
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - “In order to finally also address possible future AI applications in radiology: Our results support the idea that AI applications can assist the radiologist especially where detections, measurements and quantitative assessments are involved. The rapid and automatic detection of pathological lesions or pathological measurements is intended to reduce the rate of missed findings, but also enables quick triaging with regard to the radiologists’ reading list. Urgent cases can be presented to the radiologist in a prioritized manner after passing through AI software and thus, urgent medical interventions can be initiated earlier. The accurate assessment of image data facilitated by AI also enables the establishment of quantitative imaging biomarkers. In this way, information that is contained in image data but has not been taken into account so far can be of great use for the patient and the treating physician (for example in the form of “lab-like results”).”
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - “In conclusion, we demonstrated in a retrospective proof- of-concept setting the high potential of AI approaches to reduce the number of missed secondary findings in clinical emergency settings that require a very time-critical radiological reporting. In particular, the integration of different specialized algorithms in a single software solution is promising to avoid clinically too narrow AI applications. But also with regard to less urgent applications of medical imaging, it should be mentioned that especially non- radiology clinicians might even take more benefit from AI- assisted image analysis compared to anyway well-trained radiologists, e.g., in clinical settings without 24/7 radiology coverage or long turnaround times for radiology reporting. Although algorithms primarily need a high sensitivity to effectively reduce the number of initially missed CT findings, ongoing research should focus on algorithm improvements with regard to specificity to also reduce the number of FPs or non-relevant algorithm findings, which otherwise need to be manually ruled out by radiologists in a time-consuming procedure and also might affect radiologists’ clinical decision making.”
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - “In conclusion, we demonstrated in a retrospective proof- of-concept setting the high potential of AI approaches to reduce the number of missed secondary findings in clinical emergency settings that require a very time-critical radiological reporting. In particular, the integration of different specialized algorithms in a single software solution is promising to avoid clinically too narrow AI applications. But also with regard to less urgent applications of medical imaging, it should be mentioned that especially non- radiology clinicians might even take more benefit from AI- assisted image analysis compared to anyway well-trained radiologists, e.g., in clinical settings without 24/7 radiology coverage or long turnaround times for radiology reporting.”
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - “Although algorithms primarily need a high sensitivity to effectively reduce the number of initially missed CT findings, ongoing research should focus on algorithm improvements with regard to specificity to also reduce the number of FPs or non-relevant algorithm findings, which otherwise need to be manually ruled out by radiologists in a time-consuming procedure and also might affect radiologists’ clinical decision making.”
Reduction of missed thoracic findings in emergency whole-body computed tomography using artificial intelligence assistance
Johannes Rueckel et al.
Quant Imaging Med Surg 2021;11(6):2486-2498 - "Once a patient has left the hospital, it is incumbent on a health care system to ensure appropriate follow-up care. Between subspecialty follow-up appointments and imaging follow-up recommendations, closing the loop on an emergency department visit is an area of active development with modern advanced computing systems. There are existing plat- forms which utilize Natural language processing (NLP) and AI/ML to analyze radiology and pathology reports in real time to ensure existing best practice guidelines are being utilized and provided to ordering physicians. These systems not only guide the report generation process but also have limited ability to monitor outpatient compliance and provide alerts/ reminders to patients and primary care physicians to encourage adherence to follow-up guidelines.”
Applications of artificial intelligence in the emergency department.
Moulik SK, Kotter N, Fishman EK.
Emerg Radiol. 2020 Aug;27(4):355-358 - “Patients who presents to the ER needs to be rapidly triaged to appropriate acuity level, and when possible, the relevant diagnostic tests need to be ordered. In emergency departments the EMTALA screening is often performed by a midlevel practitioner who will assess the patient and order initial imaging and laboratory tests. This process is labor in- tensive and inherently limited in the ability of a practitioner to ingest and process a patient’s history, imaging, and prior labs in order to put the current visit in context. A rudimentary form of automation is used in these scenarios in the form of stan- dard order sets that the midlevel practitioner can select based on presenting complaint and assessment. Though expedient and relatively easy to implement, this process over-utilizes imaging and laboratory resources by creating overly broad categorizations of patients.”
Applications of artificial intelligence in the emergency department.
Moulik SK, Kotter N, Fishman EK.
Emerg Radiol. 2020 Aug;27(4):355-358 - "AI/ML systems function through the use of mathematical models which can be trained on var- ious datasets. Systems are being established for allowing general AI/ML models to be modified to better reflect localized communities through the use of techniques known as transfer learning and federated learning. In the future, AI/ML systems will likely get continuous or frequent updates of their global models while being able to maintain the specialized training that allows a general model to be broadly applicable within a narrow subgroup or community.”
Applications of artificial intelligence in the emergency department.
Moulik SK, Kotter N, Fishman EK.
Emerg Radiol. 2020 Aug;27(4):355-358 - Introduction: Despite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine.
Methods: The scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews using Medline-OVID, EMBASE, CINAHL, and IEEE, with a double screening and extrac- tion process. The search included articles published until February 28, 2020. Articles were excluded if they did not self-classify as studying an AI intervention, were not rel- evant to the emergency department (ED), or did not report outcomes or evaluation.
Results: Of the 1483 original database citations, 395 were eligible for full-text evaluation. Of these articles, a total of 150 were included in the scoping review.
Artificial intelligence in emergency medicine: A scoping review
Abirami Kirubarajan et al.
JACEP Open 2020;1:1691–1702. - “AI-related research is rapidly increasing in emergency medicine. Studies show promising opportunities for AI in diverse contexts, particularly regarding predictive modeling for patient outcomes. However, there remains uncertainty regarding their superiority over standard practice, and further research is needed before clinical implementation.”
Artificial intelligence in emergency medicine: A scoping review
Abirami Kirubarajan et al.
JACEP Open 2020;1:1691–1702. - “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 
Artificial Intelligence and Machine Learning in Emergency Medicine
Tang KJW et al.
Biocybernetics and Biomedical Engineering 41 (2021) 156–172
- Prerequisites: AI tools should not replace on-site radiological care by a local radiologist when possible. AI is not a substitute for the radiologist, as it is a simple decision-support tool. The human guarantor is indispensable, and the interpretation must be the responsibility of a radiologist with expertise in emergency imaging.
Proposals for the use of artificial intelligence in emergency radiology
Thibaut Jacques et al.
Diagnostic and Interventional Imaging 2021 (in press) - Proposal 1: the technical safety of an algorithmic solution must be guaranteed at least by a category IIa CE marking. Marking is necessary but not sufficient, as it is not intended to guarantee the real life clinical performance of a deployed algorithm.
Proposal 2: the in silico and in vivo performances of an algorithm are always different. Companies commercializing or deploying decision-support algorithms that use AI should: i), be transparent about the nature of the data used for each phase and about the confusion matrices of the algorithms under study; ii), be able to consistently provide corrected performance data based on robust extrinsic validation.
Proposals for the use of artificial intelligence in emergency radiology
Thibaut Jacques et al.
Diagnostic and Interventional Imaging 2021 (in press) - Proposal 3: after the deployment of an AI solution, routinely implement a pro-active algorithmic vigilance phase that is both ascending and descending, and strive to organize regular local Morbidity and Mortality Conferences in Artificial Intelligence (AI- MMCs) focused on algorithmic vigilance.
Proposal 4: as soon as an AI tool intended to aid medical decision is likely to be deployed, the representatives of the medical and sur- gical departments that will be impacted by the data resulting from the algorithm, in the institution, must be officially informed. This deployment must be part of the medical project of local imaging organization at the site.
Proposals for the use of artificial intelligence in emergency radiology
Thibaut Jacques et al.
Diagnostic and Interventional Imaging 2021 (in press) - Proposal 5: all the necessary measures must be implemented to limit, on the one hand, the cognitive biases of users and, on the other hand, the risk of loss of competence (“deskilling”), notably among less experienced users (residents and non-experts).
Proposal 6: the use of AI as a decision-support tool is an active process carried out by a legally responsible radiologist. This process must result in traceability and in documentation of the decision- making process. The use of such a tool for all or part of the diagnosis should be routinely recorded in the patient’s file. The working group requests that AI tools be made unable to export their data to the PACS in the absence of prior validation of this export by the radiologist in charge of the interpretation. Internal traceability of major discrepancies could be achieved by holding regular AI-MMCs.
Proposals for the use of artificial intelligence in emergency radiology
Thibaut Jacques et al.
Diagnostic and Interventional Imaging 2021 (in press)
- “The most significant takeaway from these studies may be that a need to clarify the role of AI in radiology is called for, and interest in developing curricula in AI for radiology training programs is slowly emerging. But how will it take shape? Or how will we shape it? Journals like Emergency Radiology and societies like the American Society of Emergency Radiology (ASER) have the opportunity to play important roles in defining a roadmap.”
Developing a curriculum in artificial intelligence for emergency radiology
Edmund M. Weisberg, Elliot K. Fishman
Emergency Radiology (2020) 27:359–360 - "There is no perfect answer or clear academic program ready to delineate. The technology is evolving rapidly, and even the literature that might be suitable now will be obsolete or less compelling in just a couple of years. But we should start with something. Such a rollout should begin with a course or two, based largely on a survey of the current literature and the current usage of the approved apps. At some point not far down the line, a comprehensive education policy to address AI in radiology may be appropriate, including fuller learning modules that include surveys into the complexity of deep learning and further exploration into app development and expanding the use of AI beyond the emergency radiology realm.”
Developing a curriculum in artificial intelligence for emergency radiology
Edmund M. Weisberg, Elliot K. Fishman
Emergency Radiology (2020) 27:359–360 - Technology That is Developed for Consumer Applications is Going to Revolutionize Medicine
- Voice activated
- Motion activated
- Thought activated (Star-Trek)
- Touch activated (an issue in COVID-19) - The Unknowns in AI in Radiology
- Will we get reimbursed for using AI?
- Will overall reimbursement increase or decrease?
- If AI becomes very accurate will there be a push for non-radiologists to read studies (both other physicians like ER docs and orthopedic surgeons as well as PAs) - The Unknowns in AI in Radiology
- When will AI become “standard of care” in Radiology
- What are the legal ramifications of AI (using or not using it)
- When will AI affect Radiology Residency and Fellowship training? - “With the ongoing fear of the pandemic, and the conflicting data regarding possible spread from surfaces, being able to have voice commands decrease risk and provides the ability to bypass common danger points from elevator buttons to door knobs to credit card processing machines. Outside of the individual, the increasing presence of voice-enabled devices affects the data-gathering and research approaches to this pandemic as well as to future public health crises. These devices can facilitate the sharing and gathering of information, provide near instantaneous updated information, and facilitate the pooling of data for use by public health experts and artificial intelligence algorithms.”
Connecting With Patients: The Rapid Rise of Voice Right Now
Isbitski D, Fishman EK, Rowe SP
J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17]. - "The combination of rapidly advancing voice-enabled technology and the social changes we have seen because of the coronavirus have driven the adoption of voice as a potential transformative way that patients can obtain information and communicate with their health care providers. Because of the changes that have already occurred, we can expect that we will never go back entirely to how things were and that voice will be an increasingly important influence on health care.”
Connecting With Patients: The Rapid Rise of Voice Right Now
Isbitski D, Fishman EK, Rowe SP
J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17]. - "The focus on artificial intelligence in radiology has been on the use of algorithms to enhance image interpretation and uncover imaging bio- markers. However, artificial intelligence will have profound impacts across radiology practices, and the rise of voice-enabled devices indicates that. We can expect that patient preparation, explanations of studies, and the consenting process will be well handled by voice-enabled devices with artificial intelligence algorithms.”
Connecting With Patients: The Rapid Rise of Voice Right Now
Isbitski D, Fishman EK, Rowe SP
J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17]. - "Successful practices that emerge from the coronavirus pandemic in strong positions will find ways to leverage artificial intelligence, and voice-enabled technologies can play a large role in that. Our day-to-day work in our offices will also change. Voice-enabled technologies can finally help us to realize the “paperless” office. Our phone calls, dictations, and communications with colleagues can all be done in a contactless way using voice.”
Connecting With Patients: The Rapid Rise of Voice Right Now
Isbitski D, Fishman EK, Rowe SP
J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17].