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Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Man Vs Ai Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Man vs AI

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  • Purpose: The purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls.
    Results: When 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house soft- ware decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged.
    Conclusion: Commercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.
    Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
    Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
    Abdominal Radiology (2020) 45:2469–2475
  • “This study showed that a commercially available radiomics software may be able to achieve similar diagnostic performance as an in-house radiomics software. The results obtained from one radiomics software may be transferrable to another system. Availability of commercial radiomics software may lower the barrier of entry for radiomics research and allow more researchers to engage in this exciting area of research.”
    Diagnostic performance of commercially available vs. in‐house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls
    Linda C. Chu · Berkan Solmaz · Seyoun Park · Satomi Kawamoto · Alan L. Yuille · Ralph H. Hruban · Elliot K. Fishman
    Abdominal Radiology (2020) 45:2469–2475
  • “However, there is also the potential for harm if these artificial images infiltrate our health care system by hackers with malicious intent. As proof of principle, Mirsky et al [3] showed that they were able to tamper with CT scans and artificially inject or remove lung cancers on the images. When the radiologists were blinded to the attack, this hack had a 99.2% success rate for cancer injection and a 95.8% success rate for cancer removal. Even when the radiologists were warned about the attack, the success of cancer injection decreased to 70%, but the cancer removal success rate remained high at 90%. This illustrates the sophistication and realistic appearance of such artificial images. These hacks can be targeted against specific patients or can be used as a more general attack on our radiologic data.”
    The Potential Dangers of Artificial Intelligence for Radiology and Radiologists
    Linda C. Chu, MD, Anima Anandkumar, PhD, Hoo Chang Shin, PhD, Elliot K. Fishman, MD
    JACR (in press)
  • “A generative adversarial network (GAN) is a recently developed deep- learning model aimed at creating new images. It simultaneously trains a generator and a discriminator network, which serves to generate artificial images and to discriminate real from artificial images, respectively. We have recently described how GANs can produce artificial images of people and audio content that fool the recipient into believing that they are authentic. As applied to medical imaging, GANs can generate synthetic images that can alter lesion size, location, and transpose abnormalities onto normal examinations. GANs have the potential to improve image quality, reduce radiation dose, augment data for training algorithms, and perform automated image segmentation.”
    The Potential Dangers of Artificial Intelligence for Radiology and Radiologists
    Linda C. Chu, MD, Anima Anandkumar, PhD, Hoo Chang Shin, PhD, Elliot K. Fishman, MD
    JACR (in press)
  • "However, there are several ways to mitigate potential AI-based hacks and attacks. These include clear security guide- lines and protocols that are uniform across the globe. As deep-fake technology gets more sophisticated, there is emerging research on AI-driven defense strategies. One example features the training of an AI to detect artificial images by image artifacts induced by GAN. However, AI-driven defense mechanisms have a long way to catch up, as seen in the related problem of defense against adversarial attacks. Recognizing these challenges, the Defense Advanced Research Projects Agency has launched the Media Forensics program to research against deep fakes. Hence, for now, the best defense against deep fakes is based on traditional cybersecurity best practices: secure all stages in the pipeline and enable strong encryption and monitoring tools.”
    The Potential Dangers of Artificial Intelligence for Radiology and Radiologists
    Linda C. Chu, MD, Anima Anandkumar, PhD, Hoo Chang Shin, PhD, Elliot K. Fishman, MD
    JACR (in press)
  • “We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage.”
    Human–computer collaboration for skin cancer recognition
    Philipp Tschandl et al.
    Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0
  • "We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support."
    Human–computer collaboration for skin cancer recognition
    Philipp Tschandl et al.
    Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0
  • “In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, includ- ing experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a frame- work for future studies across the spectrum of image-based diagnostics to improve human–computer collaboration in clinical practice.”
    Human–computer collaboration for skin cancer recognition
    Philipp Tschandl et al.
    Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0
  • "This study examines human–computer collaboration from multiple angles and under varying conditions. We used the domain of skin cancer recognition for simplicity, but our study could serve as a framework for similar research in image-based diagnostic medicine. In contrast to the current narrative, our findings sug- gest that the primary focus should shift from human–computer competition to human–computer collaboration. From a regulatory perspective, the performance of AI-based systems should be tested under real-world conditions in the hands of the intended users and not as stand-alone devices. Only then can we expect to rationally adopt and improve AI-based decision support and to accelerate its evolution.”
    Human–computer collaboration for skin cancer recognition
    Philipp Tschandl et al.
    Nat Med (2020). https://doi.org/10.1038/s41591-020-0942-0

  • Human–computer collaboration for skin cancer recognition
    Philipp Tschandl et al.
    Nat Med (2020).  https://doi.org/10.1038/s41591-020-0942-0
  • Background: IBM Watson for Oncology (WFO) is a cognitive computing system helping physicians quickly identify key information in a patient’s medical record, surface relevant evidence, and explore treatment options. This study assessed the possibility of using WFO for clinical treatment in lung cancer patients.
    Methods: We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer. From January to December 2018, newly diagnosed lung cancer cases in Chonnam National University Hwasun Hospital were retrospectively examined using WFO version 18.4 according to four treatment categories (surgery, radiotherapy, chemoradiotherapy, and palliative care). Treatment recommendations were considered concordant if the MDT recommendations were designated ‘recommended’ by WFO. Concordance between MDT and WFO was analyzed by Cohen’s kappa value.
    Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
    Min-Seok Kim et al.
    Transl Lung Cancer Res 2020;9(3):507-514
  • Results: In total, 405 (male 340, female 65) cases with different histology (adenocarcinoma 157, squamous cell carcinoma 132, small cell carcinoma 94, others 22 cases) were enrolled. Concordance between MDT and WFO occurred in 92.4% (k=0.881, P<0.001) of all cases, and concordance differed according to clinical stages. The strength of agreement was very good in stage IV non-small cell lung carcinoma (NSCLC) (100%, k=1.000) and extensive disease small cell lung carcinoma (SCLC) (100%, k=1.000). In stage I NSCLC, the agreement strength was good (92.4%, k=0.855). The concordance was moderate in stage III NSCLC (80.8%, k=0.622) and relatively low in stage II NSCLC (83.3%, k=0.556) and limited disease SCLC (84.6%, k=0.435). There were discordant cases in surgery (7/57, 12.3%), radiotherapy (2/12, 16.7%), and chemoradiotherapy (15/129, 11.6%), but no discordance in metastatic disease patients.
    Conclusions: Treatment recommendations made by WFO and MDT were highly concordant for lung cancer cases especially in metastatic stage. However, WFO was just an assisting tool in stage I–III NSCLC and limited disease SCLC; so, patient-doctor relationship and shared decision making may be more important in this stage..
    Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
    Min-Seok Kim et al.
    Transl Lung Cancer Res 2020;9(3):507-514
  • Methods: We evaluated the level of agreement between WFO and multidisciplinary team (MDT) for lung cancer. From January to December 2018, newly diagnosed lung cancer cases in Chonnam National University Hwasun Hospital were retrospectively examined using WFO version 18.4 according to four treatment categories (surgery, radiotherapy, chemoradiotherapy, and palliative care). Treatment recommendations were considered concordant if the MDT recommendations were designated ‘recommended’ by WFO. Concordance between MDT and WFO was analyzed by Cohen’s kappa value.
    Conclusions: Treatment recommendations made by WFO and MDT were highly concordant for lung cancer cases especially in metastatic stage. However, WFO was just an assisting tool in stage I–III NSCLC and limited disease SCLC; so, patient-doctor relationship and shared decision making may be more important in this stage..
    Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
    Min-Seok Kim et al.
    Transl Lung Cancer Res 2020;9(3):507-514
  • “In conclusion, treatment decisions made by WFO exhibited a high degree of agreement with those of the MDT tumor board, and the concordance varied by stage. AI-based CDSS is expected to play an assistive role, particularly in the metastatic lung cancer stage with less complex treatment options. However, patient-doctor relationships and shared decision making may be more important in non-metastatic lung cancer because of the complexity to reach at an appropriate decision. Further study is warranted to overcome this gray area for current machine learning algorithms.”
    Artificial intelligence and lung cancer treatment decision: agreement with recommendation of multidisciplinary tumor board
    Min-Seok Kim et al.
    Transl Lung Cancer Res 2020;9(3):507-514
  • Objective — To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.
    Conclusions — Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.
    Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
    Myura Nagendran et al.
    BMJ 2020;368:m689 doi: 10.1136/bmj.m689 (Published 25 March 2020)
  • “Deep learning AI is an innovative and fast moving field with the potential to improve clinical outcomes. Financial investment is pouring in, global media coverage is widespread, and in some cases algorithms are already at marketing and public adoption stage. However, at present, many arguably exaggerated claims exist about equivalence with or superiority over clinicians, which presents a risk for patient safety and population health at the societal level, with AI algorithms applied in some cases to millions of patients.”
    Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
    Myura Nagendran et al.
    BMJ 2020;368:m689 doi: 10.1136/bmj.m689 (Published 25 March 2020)
  • "Overpromising language could mean that some studies might inadvertently mislead the media and the public, and potentially lead to the provision of inappropriate care that does not align with patients’ best interests. The development of a higher quality and more transparently reported evidence base moving forward will help to avoid hype, diminish research waste, and protect patients.”
    Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
    Myura Nagendran et al.
    BMJ 2020;368:m689 doi: 10.1136/bmj.m689 (Published 25 March 2020)
  • “Radiologists show a rather positive attitude towards AI to become more efficient and precise, but it does not seem to make them extremely confident about their own future. Medical students also advocate the use of AI in radiology but seem to be far more pessimistic regarding danger AI represents to the profession of the diagnostic radiologist.”
    A survey on the future of radiology among radiologists, medical students T and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over
    Jasper van Hoek et al.
    European Journal of Radiology 121 (2019) 108742
  • “The diagnostic radiologist. This is also reflected in the fact that a large proportion of students answered that AI is a reason not to choose radiology as a specialty. This supposed fear might originate from a lack of information and knowledge. Following the assessment of most radiological publications – in our review of them – AI will not be a threat but rather a welcome addition to the radiological workflow. One must say that the results from our study might be worrisome. Students, and especially the best students, might not choose to go into radiology.”
    A survey on the future of radiology among radiologists, medical students T and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over
    Jasper van Hoek et al.
    European Journal of Radiology 121 (2019) 108742
  • AI and Surgical Decision Making
  • Observations  Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.
    Conclusions and Relevance  Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
    Artificial Intelligence and Surgical Decision-Making
    Tyler J. Loftus, MD1; Patrick J. Tighe, MD, MS2; Amanda C. Filiberto, MD1; et al
    JAMA Surg. Published online December 11, 2019. doi:https://doi.org/10.1001/jamasurg.2019.4917
  • Conclusions and Relevance  Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.
    Artificial Intelligence and Surgical Decision-Making
    Tyler J. Loftus, MD1; Patrick J. Tighe, MD, MS2; Amanda C. Filiberto, MD1; et al
    JAMA Surg. Published online December 11, 2019. doi:https://doi.org/10.1001/jamasurg.2019.4917
  • Even patients with substantial expertise in science or particular medical problems still rely on physicians during times of stress and uncertainty, and need them to perform procedures, interpret diagnostic tests, and prescribe medications. In these situations, reciprocal trust is central to the functioning of a health system and leads to higher treatment adherence, improvements in self-reported health, and better patient experience.So the question is: as technology continues to change relationships between patients and physicians, how can patient-physician trust be maintained or even improved?
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Prior work has examined the accuracy of AI, potential for biases, and lack of explainability (“black box”), all of which may affect physicians’ and patients’ trust in health care AI, as well as the potential for AI to replace physicians. However, in settings for which care will still be provided by a physician, whether and how AI will affect trust between physicians and patients has yet to be addressed. The potential effects of AI on trust between physicians and patients should be explicitly designed and planned for.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “When considering the implications of health care AI on trust, a broad range of health care AI applications need to be considered, including (1) use of health care AI by physicians and systems, such as for clinical decision support and system strengthening, physician assessment and training, quality improvement, clinical documentation, and nonclinical tasks, such as scheduling and notifications; (2) use of health care AI by patients including triage, diagnosis, and self-management; and (3) data for health care AI involving the routine use of patient data to develop, validate, and fine-tune health care AI as well as to personalize the output of health care AI.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Each of these applications has the potential to enable and disable the 3 components of trust: competency, motive, and transparency.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • Even patients with substantial expertise in science or particular medical problems still rely on physicians during times of stress and uncertainty, and need them to perform procedures, interpret diagnostic tests, and prescribe medications. In these situations, reciprocal trust is central to the functioning of a health system and leads to higher treatment adherence, improvements in self-reported health, and better patient experience.So the question is: as technology continues to change relationships between patients and physicians, how can patient-physician trust be maintained or even improved?
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Prior work has examined the accuracy of AI, potential for biases, and lack of explainability (“black box”), all of which may affect physicians’ and patients’ trust in health care AI, as well as the potential for AI to replace physicians. However, in settings for which care will still be provided by a physician, whether and how AI will affect trust between physicians and patients has yet to be addressed. The potential effects of AI on trust between physicians and patients should be explicitly designed and planned for.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “When considering the implications of health care AI on trust, a broad range of health care AI applications need to be considered, including (1) use of health care AI by physicians and systems, such as for clinical decision support and system strengthening, physician assessment and training, quality improvement, clinical documentation, and nonclinical tasks, such as scheduling and notifications; (2) use of health care AI by patients including triage, diagnosis, and self-management; and (3) data for health care AI involving the routine use of patient data to develop, validate, and fine-tune health care AI as well as to personalize the output of health care AI.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “Each of these applications has the potential to enable and disable the 3 components of trust: competency, motive, and transparency.”
    Promoting Trust Between Patients and Physicians in the Era of Artificial Intelligence. 
    Nundy S, Montgomery T, Wachter RM.
    JAMA. Published online July 15, 2019. doi:10.1001/jama.2018.20563
  • “To be clear, I welcome general artificial intelligence with open arms, because it will generate unprecedented prosperity for the human race just as automation has for centuries. However, it is coun- terproductive to prematurely announce its arrival. As radiologists, it behooves us to educate ourselves so that we can cut through the hype and harness the very real power of deep learning as it exists today, even with its substantial limitations. To channel Mark Twain, the reports of radiology’s demise are greatly exaggerated.”
    Why Radiologists Have Nothing to Fear From Deep Learning
    Alex Bratt
    JACR 2019 (in press)
  • ”Even when sufficient progress is made to overcome the aforementioned shortcomings, there is no reason to think that radiologists are any more likely to be displaced than artists, journalists, or CEOs, because breaking the barriers of long-term dependencies and abstract reasoning is likely to enable sweeping automation in these fields as well.”
    Why Radiologists Have Nothing to Fear From Deep Learning
    Alex Bratt
    JACR 2019 (in press)
  • Through the application of AI, information-intensive domains such as marketing, health care, financial services, education, and professional services could become simultaneously more valuable and less ex- pensive to society. Business drudgery in every indus- try and function—overseeing routine transactions, repeatedly answering the same questions, and ex- tracting data from endless documents—could become the province of machines, freeing up human workers to be more productive and creative. Cognitive tech- nologies are also a catalyst for making other data-in- tensive technologies succeed, including autonomous vehicles, the Internet of Things, and mobile and multi- channel consumer technologies.
  • Cognitive insight.
    The second most common type of project in our study (38% of the total) used algorithms to detect patterns in vast volumes of data and interpret their meaning. Think of it as “analytics on steroids.”These machine-learning applications are being used to:
    - predict what a particular customer is likely to buy;
    - identify credit fraud in real time and detect insur- ance claims fraud
    - analyze warranty data to identify safety or quality problems in automobiles and other manufactured products
    - automate personalized targeting of digital ads; and
    - provide insurers with more-accurate and detailed actuarial modeling.
  • AI in Radiology: The Bottom Line
    - AI will put Radiologists out of business
    - AI is all hype and will soon fade like many fads
    - The reality is that AI will change all aspects of Radiology but may be our savior rather the grim reaper?
  • Reality: AI is already in our patients homes (and in yours)
    - Voice-enabled assistants that use AI have entered the homes of many patients (Amazon Alexa, Google Home)
    -- Connectivity to our patients with pre-study or post-study information
    -- Can help reduce readmissions or un-necessary ER visits by answering patients questions
  • Reality: AI can eliminate needless costs
    - Eliminate positions in customer service, billing and administration
    - Eliminate significant numbers of staff in scheduling or call centers while improving the patient experience. Think Uber and Diner Reservations or even Airline reservations
  • Reality: Machine Learning can decrease medical error
    - Can AI be the ultimate second reader?
    - Clinical applications
    -- CT
    -- MR
    -- Plain Radiographs
    -- Ultrasound
    -- Pathology
  • “Second, 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
  • “Machine learning will become an indispensable tool for clinicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and clinical medicine will be challenged to grow with it.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “As in other industries, this challenge will create winners and losers in medicine. But we are optimistic that patients, whose lives and medical histories shape the algorithms, will emerge as the biggest winners as machine learning transforms clinical medicine.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “For example, a radiologist typically views 4000 images in a CT scan of multiple body parts (“pan scan”) in patients with multiple trauma. The abundance of data has changed how radiologists interpret images; from pattern recognition, with clinical context, to searching for needles in haystacks; from inference to detection. The radiologist, once a maestro with a chest ra- diograph, is now often visually fatigued searching for an occult fracture in a pan scan.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “Radiologists should identify cognitively simple tasks that could be addressed by artificial intelligence, such as screening for lung cancer on CT. This involves detecting, measuring, and characterizing a lung nodule, the management of which is standardized. A radiology residency or a medical degree is not needed to detect lung nodules.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “Because pathology and radiology have a similar past and a common destiny, perhaps these specialties should be merged into a single entity, the “information specialist,” whose responsibility will not be so much to extract information from images and histology but to manage the information extracted by artificial intelligence in the clinical context of the patient.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “The information specialist would interpret the important data, advise on the added value of another diagnostic test, such as the need for additional imaging, anatomical pathology, or a laboratory test, and integrate information to guide clinicians. Radiologists and pathologists will still be the physician’s physician.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “By virtue of its information technology-oriented infrastructure, the specialty of radiology is uniquely positioned to be at the forefront of efforts to promote data sharing across the healthcare enterprise, including particularly image sharing. The potential benefits of image sharing for clinical, research, and educational applications in radiology are immense. In this work, our group—the Association of University Radiologists (AUR) Radiology Research Alliance Task Force on Image Sharing—reviews the benefits of implementing image sharing capability, introduces current image sharing platforms and details their unique requirements, and presents emerging platforms that may see greater adoption in the future. By understanding this complex ecosystem of image sharing solutions, radiologists can become im- portant advocates for the successful implementation of these powerful image sharing resources.”


    Image Sharing in Radiology— A Primer 
Chatterjee AR et al.
Acad Radiol 2017; 24:286–294
  • “Cloud-based image sharing platforms based on interoperability standards such as the IHE-XDS-I profile are currently the most widely used method for sharing of clinical radiological images and will likely continue to grow in the coming years. Conversely, no single image sharing platform has emerged as a clear leader for research and educational applications. Radiologists, clinicians, investigators, technologists, educators, administrators, and patients all stand to benefit from medical image sharing. With their continued support, more wide- spread adoption of image sharing infrastructure will assuredly improve the standard of clinical care, research, and education in modern radiology.”

    
Image Sharing in Radiology— A Primer 
Chatterjee AR et al.
Acad Radiol 2017; 24:286–294
  • “In summary, radiologists will not be replaced by machines. Radiologists of the future will be essential data scientists of medicine. We will leverage clinical data science and ML to diagnose and treat patients better, faster, and more efficiently. Although this new clinical data science milieu will undoubtedly alter radiology practice, if performed correctly, it will empower radiologists to continue to provide better actionable recommendations on the basis of new insights from the medical images and other relevant data.”


    Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference
 Kruskal JB et al.
JACR (in press)
  • “Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records.”

    
Predicting healthcare trajectories from medical records: A deep learning approach.
Pham T et al.
J Biomed Inform. 2017 May;69:218-229. 
  • “Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk.”


    Predicting healthcare trajectories from medical records: A deep learning approach.
Pham T et al.
J Biomed Inform. 2017 May;69:218-229. 
  • “The missing piece in the dialectic around artificial intelligence and machine learning in health care is understanding the key step of separating prediction from action and recommendation. Such separation of prediction from action and recommendation requires a change in how clinicians think about using models developed using machine learning. In 2001, the statistician Breiman suggested the need to move away from the culture of assuming that models that are not causal and cannot explain the underlying process are useless. Instead, clinicians should seek a partnership in which the machine predicts (at a demonstrably higher accuracy), and the human explains and decides on action.”


    What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “The 2 cultures—computer and the physician—must work together. For example, clinicians are biased toward optimistic prediction, often overestimating life expectancy by a factor of 5, while predictive models trained from vast amounts of data do better; using these well-calibrated probability estimates of an outcome, clinicians can then can act appropriately for patients at the highest risk. The lead time a predictive model can offer to allow for an alternative action matters a great deal. Well-calibrated levels of risk for each outcome, and the timely execution of an alternative action, are needed for a model to be useful.”

    
What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “Better diagnosis, and diagnostic algorithms providing more accurate differential diagnoses, might reshape the traditional CPC (clinical problem solving) exercise, just as the development of imaging modalities and sophisticated laboratory testing made the autopsy less relevant.”


    What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “Human experts and machines have different strengths. Accordingly, there are tasks that are better suited for machines and others for humans. Some advantages of machines are that they can work 24 hours per day and contemporaneously. Also, machines may be designed to provide consistent analysis for a given input or series of input parameters. This allows for precision and potential for quantification in results reporting. Machines can analyze large volumes of data and find complex associations hidden within these data that may be otherwise difficult for a human to do.”


    Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
  • “There are a number of ways in which machine learning can help radiology practices today, including many tasks that are frequently performed by radiologists and ordering clinicians, such as imaging appropriateness assessment, creating study protocols, and standardization of radiology reporting, that could benefit from automation. Although many of these examples could be implemented using conventional procedural programming methodologies, the machine learning approach holds the promise to perform these tasks with a higher level of proficiency that can improve over time as the system “learns” new data.”

    
Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
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