google ads
Deep Learning: Deep Learning and Outcomes Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and Outcomes

-- OR --

  • “The current results found in the literature support the concept of “AI-augmented” radiologists instead of supporting the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. AI is a great aid to radiologists in the emergency setting, and improves their workflow by decreasing reading time in some areas. As the applications of AI increase, it becomes clear that the role of the radiologist may change dramatically in coming years, raising additional issues in terms of responsibility and liability.”
    Does artificial intelligence surpass the radiologist?
    Soyer P, Fishman EK, Rowe SP, Patlas MN, Chassagnon G.
    Diagn Interv Imaging. 2022 Oct;103(10):445-447. 
  • “However, all these advances must be interpreted with caution. Virtually all studies about AI were made retrospectively and more research is needed to make sure than the use of AI provides equivalent results in real word prospective studies. Moreover, the added value of AI in radiology should be evaluated using other metrics than sensitivity only, but also in terms of level of confidence of a given diagnosis, faster workflow, improved patient management, and better work life balance for the radiologists. Weak AI will continue to dominate the current landscape until strong AI becomes a more relevant reality − at which point, it will be impossible to predict the implications for radiology and society at large. To date, we can say that the final diagnosis is still the specific task and the responsibility of the radiologist.”
    Does artificial intelligence surpass the radiologist?
    Soyer P, Fishman EK, Rowe SP, Patlas MN, Chassagnon G.
    Diagn Interv Imaging. 2022 Oct;103(10):445-447. 
  • “There are other fields in which AI seriously challenges the radiologist. In this regard, Romero-Martin et al. evaluated the standalone performance of an AI system (Transpara, version 1.7.0; ScreenPoint Medical) as an independent reader of digital mammography or digital breast tomosynthesis screening examinations. These researchers found that AI could replace radiologists' readings in breast screening, achieving a noninferior sensitivity compared to single or double human reading for digital mammography (62.8% vs. 58.4% and 67.3%, respectively; P = 0.458 and 0.523), with a lower recall rate. For digital breast tomosynthesis, AI yielded noninferior sensitivity compared to single or double human reading (80.5% vs. 77.0% and 81.4%, respectively; P = 0.648) but with a greater recall rate .Although this study performed in a real word scenario has limitations, it raises some serious questions about the systematic implementation of AI in the field of breast screening in the near future.”
    Does artificial intelligence surpass the radiologist?
    Soyer P, Fishman EK, Rowe SP, Patlas MN, Chassagnon G.
    Diagn Interv Imaging. 2022 Oct;103(10):445-447. 
  • “As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitoredand maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.”
    Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
    Dania Daye et al.
    Radiology 2022; 000:1–9 
  • “An imaging AI governing body has the responsibilities of defining the purposes, priorities, strategies, and scope of the group; establishing a framework for operation; and linking those to the organizational mission, values, vision, and strategy. AI governance structures provide mechanisms to decide which tools should be deployed locally and how to best allocate institutional and/or departmental resources to support the clinical implementation of the most valuable and highest-impact applications to improve patient care. Governance committees can establish a robust process to score and evaluate AI-based solutions objectively.”
    Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
    Dania Daye et al.
    Radiology 2022; 000:1–9 
  • Summary
    Successful clinical implementation of artificial intelligence is facilitatedby establishing robust organizational structures to ensure appropriateoversight of algorithm implementation, maintenance, and monitoring. Essentials
    • Clinical imaging artificial intelligence (AI) programs require four components for successful implementation: data access and security, cross-platform and cross-domain integration, clinical translation and delivery, and leadership that supports innovation.
    • Oversight of AI in medical imaging should consider stakeholders across multiple disciplines who use radiology services.
    • AI governance should address the factors used when assessing an algorithm for implementation, different implementation models, and model monitoring and maintenance after implementation.
    Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
    Dania Daye et al.
    Radiology 2022; 000:1–9 
  • “AI governance bodies must interface both with AI industry partners and enterprise information technology support teams to ensure successful implementation of AI models. The AI intake process will still require formal assessment, preferably quantified, with consideration of clinical safety and benefits, implementation complexity, and business aspects to assess the expected impact of implementation. After approval by the governance committee, a group of end users should have the opportunity to provide formal feedback on the approved algorithm, especially relating to the user interface and user experience, the use of the algorithms, and integration into the clinical workflow.”  
    Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
    Dania Daye et al.
    Radiology 2022; 000:1–9 
  • “We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist’s report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice.”
    Is AI the Ultimate QA?  
    Edmund M. Weisberg · Linda C. Chu · Benjamin D. Nguyen · Pelu Tran · Elliot K. Fishman
    Journal of Digital Imaging https://doi.org/10.1007/s10278-022-00598-8 
  • “A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist’s report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice.”
    Is AI the Ultimate QA?  
    Edmund M. Weisberg · Linda C. Chu · Benjamin D. Nguyen · Pelu Tran · Elliot K. Fishman
    Journal of Digital Imaging https://doi.org/10.1007/s10278-022-00598-8 
  • "Currently, AI for imaging occurs most frequently at the point of care. Whether for triage or as second or even a primary reader, AI tends to be built directly into clinical workflows for computed tomography (CT) scans. While computer- aided detection (CAD) use has grown, it is still far from ubiquitous, and concerns have been raised regarding radiologist over-reliance on these powerful but flawed tools. More than 90% of algorithms are trained on patients from California, Massachusetts, and New York. And more than 90% of algorithms are inappropriately validated.”
    Is AI the Ultimate QA?  
    Edmund M. Weisberg · Linda C. Chu · Benjamin D. Nguyen · Pelu Tran · Elliot K. Fishman
    Journal of Digital Imaging https://doi.org/10.1007/s10278-022-00598-8 
  • "Quality improvement initiatives and the peer review process are protected and nondiscoverable federally under the Patient Safety and Quality Improvement Act (PSQIA) of 2005. AI in the quality workflow is viewed only a by peer reviewer operating under the quality committee and not by the reading radiologist and thus falls under quality committee protections.”
    Is AI the Ultimate QA?  
    Edmund M. Weisberg · Linda C. Chu · Benjamin D. Nguyen · Pelu Tran · Elliot K. Fishman
    Journal of Digital Imaging https://doi.org/10.1007/s10278-022-00598-8 
  • "The effectiveness of AI in the background depends on the accuracy of the detection algorithm and the NLP, since errors in either component will affect overall performance. Questions remain as to how well the NLP will work. Is the system sophisticated enough to differentiate the language used by radiologists in reports? One new report suggests that the work of AI in NLP has great room for improvement. As radiologists move towards structured reporting and standardization of terminology, the performance of the NLP may further improve in the future. This will be critical to minimize both false positive and false negative studies.”
    Is AI the Ultimate QA?  
    Edmund M. Weisberg · Linda C. Chu · Benjamin D. Nguyen · Pelu Tran · Elliot K. Fishman
    Journal of Digital Imaging https://doi.org/10.1007/s10278-022-00598-8 
  • “There are still open questions about exactly how AI assistance affects human performance. For instance, AI assistance has sometimes been shown to improve clinical experts’ sensitivity while lowering their specificity, and some studies, both prospective and retrospective, have found that combined AI–human performance could not surpass the performance of AI alone. Furthermore, some clinicians may benefit more from AI assistance than others; studies suggest that less experienced clinicians, such as trainees, benefit more from AI input than their more experienced peers.“
    AI in health and medicine
    Pranav Rajpurkar, Emma Chen, Oishi Banerjee and Eric J. Topol  
    NATURE MEDICINE | VOL 28 | January 2022 | 31–38 | 
  • "Deep learning has also made progress in gastroenterology, especially in terms of improving colonoscopy, a key procedure used to detect colorectal cancer. Deep learning has been used to automatically predict whether colonic lesions are malignant, with performance comparable to skilled endoscopist. Additionally, because polyps and other possible signs of disease are frequently missed during the exam, AI systems have been developed to assist endoscopists. Such systems have been shown to improve endoscopists’ ability to detect irregularities, potentially improving sensitivity and making colonoscopy a more reliable tool for diagnosis.”
    AI in health and medicine
    Pranav Rajpurkar, Emma Chen, Oishi Banerjee and Eric J. Topol  
    NATURE MEDICINE | VOL 28 | January 2022 | 31–38 | 
  • “Overfitting is a major obstacle for AI technology, but what exactly, is overfitting? Burnham describes “the essence of overfitting is to have unknowingly extracted some of the residual variation as if that variation represented underlying model structure” . In layman's terms, overfitting means that an AI model has learned in a manner that is only applicable to the training sample and is no longer generalizable to the overall population .”
    Understanding artificial intelligence based radiology studies: What is overfitting?
    Simukayi Mutasa, Shawn Sun, Richard Ha
    Clinical Imaging 65 (2020) 96–99
  • "For example, if an algorithm designed to distinguish between dogs and cats is trained only with the German shepherd dogs and Siamese cats in, it will perform well if subsequently tested only on German shepherd dogs and Siamese cats. However, if the algorithm is then asked to distinguish other types of dogs and cats, which it has not seen before, its performance will decrease substantially.”
    Understanding artificial intelligence based radiology studies: What is overfitting?
    Simukayi Mutasa, Shawn Sun, Richard Ha
    Clinical Imaging 65 (2020) 96–99
  • “The exciting results of recent AI radiology studies certainly generate much anticipation towards a future where radiologists utilize AI to better save lives. However, the pitfall of overfitting really highlights the need for external validation of AI before clinical implementation. There have been cases of neural network performance being affected by data from a different institution . To prove to clinicians the validity of results, deep neural networks need to de- monstrate performance on external data different from its training data. Some researchers have even emphasized the need for prospective, multi-center, cohort studies and to hold AI technology to the same level of scrutiny as new clinical drugs. Undoubtedly, the field of AI in medical imaging is still in its infancy, as studies achieving that level of validation are extremely rare.”
    Understanding artificial intelligence based radiology studies: What is overfitting?
    Simukayi Mutasa, Shawn Sun, Richard Ha
    Clinical Imaging 65 (2020) 96–99
  • "What seems ethically imperative at present, though, is a steady and informed rebuttal of AI hype, especially as it is aimed at image-dependent technologies like radiology. Today’s hospitals simply cannot function without radiologists, who are core to their diagnostic functions. To allow a deterioration in the quality of radiology services because of the promulgation of false narratives imperils the public welfare. Rather than being caricatured as in a state of near-future extinction, radiology might well advance to a new era of excellence.”
    AI Hype and Radiology: A Plea for Realism and Accuracy
    Banja J et al.
    Radiology: Artificial Intelligence 2020; 2(4):e190223
  • "However, perhaps a better explanation as to why innovation in AI may be slowing is that much of the private sector seems frankly disinterested. Today’s deep learning models appear in- creasingly focused on merchandizing applications that forecast product demand and facilitate sales rather than on humanitarian welfare concerns.”
    AI Hype and Radiology: A Plea for Realism and Accuracy
    Banja J et al.
    Radiology: Artificial Intelligence 2020; 2(4):e190223
  • “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

  • Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Question  Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings  In this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning  In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Objectives  To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer.
    Design, Setting, and Participants  Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Conclusions and Relevance  In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • “This cohort study demonstrated that, in a large heterogeneous population of patients seeking outpatient oncology care, ML algorithms based on structured real-time EHR data had adequate performance in identifying outpatients with cancer who had high risk of short-term mortality. According to clinician surveys, most patients flagged as having high risk by one of the ML models were appropriate for a timely conversation about goals and end-of-life preferences. Our findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values.”
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Key Points
    Question Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings In this cohort study of 26525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.

  • Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Question  Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings  In this cohort study of 26 525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning  In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Objectives  To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer.
    Design, Setting, and Participants  Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Conclusions and Relevance  In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • “This cohort study demonstrated that, in a large heterogeneous population of patients seeking outpatient oncology care, ML algorithms based on structured real-time EHR data had adequate performance in identifying outpatients with cancer who had high risk of short-term mortality. According to clinician surveys, most patients flagged as having high risk by one of the ML models were appropriate for a timely conversation about goals and end-of-life preferences. Our findings suggest that ML tools hold promise for integration into clinical workflows to ensure that patients with cancer have timely conversations about their goals and values.”
    Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer. 
    Parikh RB, Manz C, Chivers C, et al.
    JAMA Netw Open. Published online October 25, 20192(10):e1915997. doi:10.1001/jamanetworkopen.2019.15997
  • Key Points
    Question Can machine learning algorithms identify oncology patients at risk of short-term mortality to inform timely conversations between patients and physicians regrading serious illness?
    Findings In this cohort study of 26525 patients seen in oncology practices within a large academic health system, machine learning algorithms accurately identified patients at high risk of 6-month mortality with good discrimination and positive predictive value. When the gradient boosting algorithm was applied in real time, most patients who were classified as having high risk were deemed appropriate by oncology clinicians for a conversation regarding serious illness.
    Meaning In this study, machine learning algorithms accurately identified patients with cancer who were at risk of 6-month mortality, suggesting that these models could facilitate more timely conversations between patients and physicians regarding goals and values.

Privacy Policy

Copyright © 2022 The Johns Hopkins University, The Johns Hopkins Hospital, and The Johns Hopkins Health System Corporation. All rights reserved.
CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning