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

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

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  • “The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethical requirements to protect patient privacy. In medical imaging, harmonized data exchange formats such as Digital Imaging and Communication in Medicine and electronic data storage are the standard, partially addressing the first issue, but the requirements for privacy preservation are equally strict. To prevent patient privacy compromise while promoting scientific research on large datasets that aims to improve patient care, the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging applications, alongside potential attack vectors and future prospects in medical imaging and beyond.”
    Secure, privacy-preserving and federated machine learning in medical imaging
    Georgios A. Kaissis et al.
    Nature Machine Intelligence
    Nat Mach Intell 2, 305–311 (2020). 
  • "We believe that the widespread adoption of secure and private AI will require targeted multi-disciplinary research and investment in the following areas. Decentralized data storage and federated learning systems, replacing the current paradigm of data sharing and centralized storage, have the greatest potential to enable privacy-preserving cross-institutional research in a breadth of biomedical disciplines in the near future, with results in medical imaging and genomics recently demonstrated.”
    Secure, privacy-preserving and federated machine learning in medical imaging
    Georgios A. Kaissis et al.
    Nature Machine Intelligence
    Nat Mach Intell 2, 305–311 (2020). 

  • Secure, privacy-preserving and federated machine learning in medical imaging
    Georgios A. Kaissis et al.
    Nature Machine Intelligence Nat Mach Intell 2, 305–311 (2020). 
  • “Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to “sell” clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed.”
    Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework
    David B.Larson et al.
    Radiology 2020; 00:1–8
  • "The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly fol- lowed. Rather than debate whether patients or provider organizations “own” the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.”
    Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework
    David B.Larson et al.
    Radiology 2020; 00:1–8
  • “Medical artificial intelligence (AI) can perform with expert-level accuracy and deliver cost-effective care at scale. IBM’s Watson diagnoses heart disease better than cardiologists do. Chatbots dispense medical advice for the United Kingdom’s National Health Service in lieu of nurses. Smartphone apps now detect skin cancer with expert accuracy. Algorithms identify eye diseases just as well as specialized physicians. Some forecast that medical AI will pervade 90% of hospitals and replace as much as 80% of what doctors currently do. But for that to come about, the health care system will have to overcome patients’ distrust of AI.”
    AI Can Outperform Doctors. So Why Don’t Patients Trust It?
    Chiara Longoni and Carey K. Morewedge
    Harvard Business Review Oct 30, 2019
  • “The reason, we found, is not the belief that AI provides inferior care. Nor is it that patients think that AI is more costly, less convenient, or less informative. Rather, resistance to medical AI seems to stem from a belief that AI does not take into account one’s idiosyncratic characteristics and circumstances. People view themselves as unique, and we find that this belief includes their health. Other people experience a cold; “my” cold, however, is a unique illness that afflicts “me” in a distinct way. By contrast, people see medical care delivered by AI providers as inflexible and standardized — suited to treat an average patient but inadequate to account for the unique circumstances that apply to an individual.”
    AI Can Outperform Doctors. So Why Don’t Patients Trust It?
    Chiara Longoni and Carey K. Morewedge
    Harvard Business Review Oct 30, 2019
  • "There are a number of steps that care providers can take to overcome patients’ resistance to medical Al. For example, providers can assuage concerns about being treated as an average or a statistic by taking actions that increase the perceived personalization of the care delivered by AI. When we explicitly described an AI provider as capable of tailoring its recommendation for whether to undergo coronary bypass surgery to each patient’s unique characteristics and medical history, study participants reported that they would be as likely to follow the treatment recommendations of the AI provider as they would be to follow the treatment recommendations of a human physician.”
    AI Can Outperform Doctors. So Why Don’t Patients Trust It?
    Chiara Longoni and Carey K. Morewedge
    Harvard Business Review Oct 30, 2019
  • “AI-based health care technologies are being developed and deployed at an impressive rate. AI- assisted surgery could guide a surgeon’s instrument during an operation and use data from past operations to inform new surgical techniques. AI-based telemedicine could provide primary care support to remote areas without easy access to health care. Virtual nursing assistants could interact with patients 24/7, offer round-the-clock monitoring, and answer questions. But harnessing the full potential of these and other consumer-facing medical AI services will require that we first overcome patients’ skepticism of having an algorithm, rather than a person, making decisions about their care.”
    AI Can Outperform Doctors. So Why Don’t Patients Trust It?
    Chiara Longoni and Carey K. Morewedge
    Harvard Business Review Oct 30, 2019
  • “One element of AI’s uniqueness is actually its vulnerability. Algorithms are sensitive to the ground truth, formerly called “gold standard.” Thus, labeled data have value in the market, however that market place emerges. To take this concept further, properly labeling datasets with ground truth, an onerous task requiring labor, is valuable. Labeled data is a currency of sorts. The corollary is that publicly-available datasets, which are extensively used by researchers, should be taken with a pinch of salt.”
    Artificial Intelligence in Radiology–– The State of the Future
    Jha S, Cook T
    Acad Radiol 2020; 27:1–2
  • “It could be argued that algorithms trained on vast amounts of individual-level data are un- wieldy or even superfluous. Who needs an algorithm to suggest the same decisions people would make themselves? Such a function might become critical, however, when choices have to be made, for instance, regarding continued life support for someone who can no longer make decisions."
    Algorithm-Aided Prediction of Patient Preferences — An Ethics Sneak Peek
    Nikola Biller‐Andorno, Armin Biller
    n engl j med 381;15
  • "Conceiving of AI as a substitute for human decision making is challenging from a technical point of view. Examining the relationship between AI and decision making, Jean-Charles Pomerol has delineated two major aspects of decision making: diagnosis and “look ahead.” Diagnosis involves pattern matching and is therefore perfectly amenable to AI. Look ahead requires both the ability to combine many actions and events and the ability to anticipate all possible reactions.”
    Algorithm-Aided Prediction of Patient Preferences — An Ethics Sneak Peek
    Nikola Biller‐Andorno, Armin Biller
    n engl j med 381;15
  • "The prospect that algorithms may compound the effects of evidence-based medicine, guide- lines, and budget targets in limiting the scope available for individual clinical judgment is dis- concerting to clinicians who believe that their professionalism is under threat.26 The American Medical Association addresses this point by con- ceiving of AI not as artificial intelligence but as “augmented intelligence” that enhances rather than replaces physicians’ expertise.”
    Algorithm-Aided Prediction of Patient Preferences — An Ethics Sneak Peek
    Nikola Biller‐Andorno, Armin Biller
    n engl j med 381;15
  • “Algorithms may prompt us to revisit some questions that ethicists have long puzzled over, such as how we can know what a good ethical decision is. They also raise new ones: Will algorithms end up making better, more reliable, and more consistent moral choices than humans do? What can we learn from algorithms to improve our ethical reasoning and decision-making skills?"
    Algorithm-Aided Prediction of Patient Preferences — An Ethics Sneak Peek
    Nikola Biller‐Andorno, Armin Biller
    n engl j med 381;15
  • “Radiologists will remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem.”
    Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement
    J. Raymond Geis et al.
    Insights into Imaging (2019) 10:101
  • “AI-based health care technologies are being developed and deployed at an impressive rate. AI- assisted surgery could guide a surgeon’s instrument during an operation and use data from past operations to inform new surgical techniques. AI-based telemedicine could provide primary care support to remote areas without easy access to health care. Virtual nursing assistants could interact with patients 24/7, offer round-the-clock monitoring, and answer questions. But harnessing the full potential of these and other consumer-facing medical AI services will require that we first overcome patients’ skepticism of having an algorithm, rather than a person, making decisions about their care.”
    AI Can Outperform Doctors. So Why Don’t Patients Trust It?
    Chiara Longoni and Carey K. Morewedge
    Harvard Business Review October 2019
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