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Deep Learning: Deep Learning and the Fda (regulations) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and the FDA (regulations)

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  • AI and the FDA
  • AI and the FDA
  • AI and the FDA
  • AI and the FDA
  • AI and the FDA
  • “On the other hand, machine learning (ML) algorithms—also referred to as a data-based approach—“learn”  from numerous examples in a dataset without being explicitly programmed to reach a particular answer or conclusion. ML algorithms can learn to decipher patterns in patient data at scales larger than a human can analyze while also potentially uncovering previously unrecognized correlations. Algorithms may also work at a faster pace than a human.”
    How FDA Regulates Artificial Intelligence in  Medical Products
    Pew Charitable Trusts July 2021 
  • "Most ML-driven applications use a supervised approach in which the data used to train and validate the algorithm is labeled in advance by humans; for example, a collection of chest X-rays taken of people who have lung cancer and those who do not, with the two groups identified for the AI software. The algorithm examines all examples within the training dataset to “learn” which features of a chest X-ray are most closely correlated with the diagnosis of lung cancer and uses that analysis to predict new cases. Developers then test the algorithm to see how generalizable it is; that is, how well it performs on a new dataset, in this case, a new set of chest X-rays. Further validation is required by the end user, such as the health care practice, to ensure that the algorithm is accurate in real-world settings.”
    How FDA Regulates Artificial Intelligence in  Medical Products
    Pew Charitable Trusts July 2021 
  • "Locked algorithms can degrade as new treatments and clinical practices arise or as populations alter overtime. These inevitable changes may make the real-world data entered into the AI program vastly different from its training data, leading the software to yield less accurate results. An adaptive algorithm could present an advantage in such situations, because it may learn to calibrate its recommendations in response to new data, potentially becoming more accurate than a locked model. However, allowing an adaptive algorithm to learn and adapt on its own also presents risks, including that it may infer patterns from biased practices or underperform in small subgroups of patients.”
    How FDA Regulates Artificial Intelligence in  Medical Products
    Pew Charitable Trusts July 2021 
  • "In addition, patients are often not aware when an AI program has influenced the course of their care; these tools could, for example, be part of the reason a patient does not receive a certain treatment or is recommended fora potentially unnecessary procedure. Although there are many aspects of health care that a patient may not fully understand, in a recent patient engagement meeting hosted by FDA, some committee members—including patient advocates—expressed a desire to be notified when an AI product is part of their care. This desire included knowing if the data the model was trained on was representative of their particular demographics, or if it had been modified in some way that changed its intended use.”
    How FDA Regulates Artificial Intelligence in  Medical Products
    Pew Charitable Trusts July 2021 
  • “An innovative framework proposed by the FDA seeks to address these issues by looking to current good manufacturing practices (cGMP) and adopting a total product lifecycle (TPLC) approach. If brought into force, this may reduce the regulatory burden incumbent on developers, while holding them to rigorous quality standards, maximizing safety, and permitting the field to mature.”
    How the FDA Regulates AI
    Harvey BH, Gowda V
    Acad Radiol 2020; 27:58–61
  • "The integration of man and machine to drive outcomes is not a concept particularly new to radiology. However, AI poses unique regulatory issues which set it apart from other advances in imaging technology. Unlike the case for the majority of pharmaceutical products, devices, and foods, the FDA has indicated its preference to regulate AI software based on function, rather than technical components or indicated use. Consequently, medical products incorporating AI will likely straddle the boundaries delineated in decent guidance documents and find incorporation into both CDS and regulated devices necessitating conventional premarket review.”
    How the FDA Regulates AI
    Harvey BH, Gowda V
    Acad Radiol 2020; 27:58–61
  • “As the market grows, the FDA will likely promulgate new regulations with a greater degree of specificity than those currently in existence, particularly in the realms of data security and privacy. This is critical to cloud- based systems susceptible to cyberattack. Further research in these and other areas will dynamically inform policymaking as the field matures.”
    How the FDA Regulates AI
    Harvey BH, Gowda V
    Acad Radiol 2020; 27:58–61
  • OBJECTIVE. Although extensive attention has been focused on the enormous potential of artificial intelligence (AI) technology, a major question remains: how should this fundamentally new technology be regulated? The purpose of this article is to provide an overview of the pathways developed by the U.S. Food and Drug Administration to regulate the incorporation of AI in medical imaging.
    CONCLUSION. AI is the new wave of innovation in health care. The technology holds promising applications to revolutionize all aspects of medicine.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Unlike the regulation of drugs and devices, the regulation of AI by the FDA poses unique challenges. In its Digital Health In- novation Action Plan, the FDA acknowledged that the traditional approach to evaluating hardware-based medical devices is not suited for the faster iterative design of software- based medical technologies. This is partly because of the inherent variability in the parameters of AI-based technologies, which depend on both the nature and the source of the data. For example, in a recent study on deep learning algorithms for the auto- mated detection of an anterior cruciate ligament tear on knee MRI, the algorithm had an AUC value of 0.824 for an external test dataset and an AUC value of 0.937 for an internal test dataset. The veracity of algorithms would have to be judged by two witnesses, so to speak.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Traditional image processing techniques were rule based and predictable, and they relied on well-defined features such as the size, texture, and heterogeneity of a lesion. AI- based technologies often use deep learning in which large amounts of data are fed into a computer system and the computer develops rules to predict outcomes from the data. A technology that learns on its own has an explainability problem—that is, we do not know how it arrived at the rules it derived from the data. The explainability problem makes it difficult to benchmark AI.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • ”Class I devices, which are classified as low risk, are typically exempt from PMA review; an example of a class I device is an algorithm that merely labels nodules in a chest CT, rather than stating which nodule is malignant, so the nodules are brought to the attention of radiologists. Most AI algorithms are categorized as class I devices or are excluded from being designated as a device as outlined by the recent 21st Century Cures Act updated draft guidelines.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Technology that lacks a predicate device (i.e., a predecessor) is a revolutionary technology. Because the technology is brand new, no evidence has accrued that can form a framework for regulation. Although AI- based imaging algorithms are fairly new, they can be either evolutionary or revolutionary. Quantification of coronary calcium or detection of a lung nodule with the use of machine learning techniques would be con- sidered evolutionary because these tasks have already been performed by software using rule-based automatic and semiautomatic methods.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided detection—CAD systems flag abnormalities for review by radiologists but do not assist in diagnostic or clinical decision making. They focus on the detection of abnormalities rather than their characterization. Examples of CAD include identification of colonic polyps on CT colonography, filling defects on pulmonary embolism CT, or liver lesions on CT or MRI. Critically, CAD analysis does not include further analysis of these lesions; instead, it flags a finding for clinician review but does not directly make a diagnosis of colon cancer, pulmonary embolism, hepatic malignancy, or other abnormality.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided diagnosis—CADx systems take analytics to a higher level than CAD systems. The FDA characterizes CADx not only as identifying the disease but also as providing an assessment of the disease through either a specific diagnosis or differential diagnosis as well as determining the extent of disease, the prognosis, and the presence of other known conditions. Thus, CADx involves the role of CAD, al- though the opposite is not true. As an example, CADx technology might identify lung nodules on CT (CAD) and might also pro- vide a malignancy score for those lesions (CADx)”.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “PMA is the most stringent of the approval pathways. PMA approval is based on a deter- mination by the FDA that there is sufficient valid scientific evidence to ensure that the device is safe and effective for its intended use. This generally requires rigorous nonclinical and clinical studies to be conducted that show evidence of safety and efficacy in a substantial population. This is generally the pathway for class III devices that are considered high risk for patients or those that are revolutionary.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI may potentially revolutionize health care, it is often considered only evolutionary from the FDA’s point of view, because often a predicate de- vice can be identified so that the demanding PMA process can be avoided and a 510(k) approach can be pursued. For instance, newer image postprocessing algorithms that use deep learning have used commercially available postprocessing software that does not use deep learning as a predicate, and these algorithms have gone through the 510(k) pathway for FDA approval.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “More recently, the FDA developed the Digital Health Software Precertification (Pre-Cert) Program. This program is based on the assumption that because medical soft- ware evolves so rapidly, every iteration of a particular technology cannot realistically be reviewed by the FDA. This approach specifically regulates software by primarily evaluating the developer of the product rather than the product itself, thus deviating from the traditional approval processes that directly evaluated a particular product.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “The Pre-Cert program mirrors the Transportation Security Administration (TSA) Pre-Check program, because prevented companies are given a higher level of trust after meeting certain rigorous certification criteria. Several participants, including ma- jor consumer electronic companies, have already been enrolled in an early pilot version of this program. The participants will pro- vide the FDA access to the measures they use to develop, test, and maintain software products, including ways that they collect post- market data. After attaining certification, they will then undergo periodic audits rather than constant stepwise reviews as their dynamic products change. This approach may be a key solution to the rapid nature of software development and the associated workload burdens affecting the approval system.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Medical device companies generally take one of three paths to gain regulatory approval: they seek approval in the United States first, seek approval overseas first, or seek approval in the United States and overseas in tandem. To develop a viable business strategy, a medical device company must understand the strengths and weaknesses of the regulatory system, its target market, the amount of internal and external resources required, and the amount of reimbursement available. In general, release in the United States requires a higher capital investment but gives a company access to the widest market, better intellectual property protection, and less foreign competition.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI algorithms pose a unique challenge to medical regulation agencies, these challenges are being acknowledged and addressed by the FDA, which recognizes that the standards by which medical tech- nology is evaluated may not apply to AI. By creating novel regulatory pathways, the FDA is encouraging the adoption of AI in medicine. The exact regulatory pathway and burden will be determined by intent—that is, whether AI is used for detection or diagnosis and whether is it used as an adjunct or a replacement. Regulatory standards are likely to evolve as AI algorithms become more robust and widespread.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Unlike the regulation of drugs and devices, the regulation of AI by the FDA poses unique challenges. In its Digital Health In- novation Action Plan, the FDA acknowledged that the traditional approach to evaluating hardware-based medical devices is not suited for the faster iterative design of software- based medical technologies. This is partly because of the inherent variability in the parameters of AI-based technologies, which depend on both the nature and the source of the data. For example, in a recent study on deep learning algorithms for the auto- mated detection of an anterior cruciate ligament tear on knee MRI, the algorithm had an AUC value of 0.824 for an external test dataset and an AUC value of 0.937 for an internal test dataset. The veracity of algorithms would have to be judged by two witnesses, so to speak.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Traditional image processing techniques were rule based and predictable, and they relied on well-defined features such as the size, texture, and heterogeneity of a lesion. AI- based technologies often use deep learning in which large amounts of data are fed into a computer system and the computer develops rules to predict outcomes from the data. A technology that learns on its own has an explainability problem—that is, we do not know how it arrived at the rules it derived from the data. The explainability problem makes it difficult to benchmark AI.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • ”Class I devices, which are classified as low risk, are typically exempt from PMA review; an example of a class I device is an algorithm that merely labels nodules in a chest CT, rather than stating which nodule is malignant, so the nodules are brought to the attention of radiologists. Most AI algorithms are categorized as class I devices or are excluded from being designated as a device as outlined by the recent 21st Century Cures Act updated draft guidelines.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Technology that lacks a predicate device (i.e., a predecessor) is a revolutionary technology. Because the technology is brand new, no evidence has accrued that can form a framework for regulation. Although AI- based imaging algorithms are fairly new, they can be either evolutionary or revolutionary. Quantification of coronary calcium or detection of a lung nodule with the use of machine learning techniques would be con- sidered evolutionary because these tasks have already been performed by software using rule-based automatic and semiautomatic methods.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided detection—CAD systems flag abnormalities for review by radiologists but do not assist in diagnostic or clinical decision making. They focus on the detection of abnormalities rather than their characterization. Examples of CAD include identification of colonic polyps on CT colonography, filling defects on pulmonary embolism CT, or liver lesions on CT or MRI. Critically, CAD analysis does not include further analysis of these lesions; instead, it flags a finding for clinician review but does not directly make a diagnosis of colon cancer, pulmonary embolism, hepatic malignancy, or other abnormality.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Computer-aided diagnosis—CADx systems take analytics to a higher level than CAD systems. The FDA characterizes CADx not only as identifying the disease but also as providing an assessment of the disease through either a specific diagnosis or differential diagnosis as well as determining the extent of disease, the prognosis, and the presence of other known conditions. Thus, CADx involves the role of CAD, al- though the opposite is not true. As an example, CADx technology might identify lung nodules on CT (CAD) and might also pro- vide a malignancy score for those lesions (CADx)”.
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “PMA is the most stringent of the approval pathways. PMA approval is based on a deter- mination by the FDA that there is sufficient valid scientific evidence to ensure that the device is safe and effective for its intended use. This generally requires rigorous nonclinical and clinical studies to be conducted that show evidence of safety and efficacy in a substantial population. This is generally the pathway for class III devices that are considered high risk for patients or those that are revolutionary.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI may potentially revolutionize health care, it is often considered only evolutionary from the FDA’s point of view, because often a predicate de- vice can be identified so that the demanding PMA process can be avoided and a 510(k) approach can be pursued. For instance, newer image postprocessing algorithms that use deep learning have used commercially available postprocessing software that does not use deep learning as a predicate, and these algorithms have gone through the 510(k) pathway for FDA approval.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “More recently, the FDA developed the Digital Health Software Precertification (Pre-Cert) Program. This program is based on the assumption that because medical soft- ware evolves so rapidly, every iteration of a particular technology cannot realistically be reviewed by the FDA. This approach specifically regulates software by primarily evaluating the developer of the product rather than the product itself, thus deviating from the traditional approval processes that directly evaluated a particular product.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “The Pre-Cert program mirrors the Transportation Security Administration (TSA) Pre-Check program, because prevented companies are given a higher level of trust after meeting certain rigorous certification criteria. Several participants, including ma- jor consumer electronic companies, have already been enrolled in an early pilot version of this program. The participants will pro- vide the FDA access to the measures they use to develop, test, and maintain software products, including ways that they collect post- market data. After attaining certification, they will then undergo periodic audits rather than constant stepwise reviews as their dynamic products change. This approach may be a key solution to the rapid nature of software development and the associated workload burdens affecting the approval system.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Medical device companies generally take one of three paths to gain regulatory approval: they seek approval in the United States first, seek approval overseas first, or seek approval in the United States and overseas in tandem. To develop a viable business strategy, a medical device company must understand the strengths and weaknesses of the regulatory system, its target market, the amount of internal and external resources required, and the amount of reimbursement available. In general, release in the United States requires a higher capital investment but gives a company access to the widest market, better intellectual property protection, and less foreign competition.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “Although AI algorithms pose a unique challenge to medical regulation agencies, these challenges are being acknowledged and addressed by the FDA, which recognizes that the standards by which medical tech- nology is evaluated may not apply to AI. By creating novel regulatory pathways, the FDA is encouraging the adoption of AI in medicine. The exact regulatory pathway and burden will be determined by intent—that is, whether AI is used for detection or diagnosis and whether is it used as an adjunct or a replacement. Regulatory standards are likely to evolve as AI algorithms become more robust and widespread.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:886–888
  • “More recently, the FDA developed the Digital Health Software Precertification (Pre-Cert) Program. This program is based on the assumption that because medical software evolves so rapidly, every iteration of a particular technology cannot realistically be reviewed by the FDA. This approach specifically regulates software by primarily evaluating the developer of the product rather than the product itself, thus deviating from the traditional approval processes that directly evaluated a particular product.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:1–3
  • “The Pre-Cert program mirrors the Transportation Security Administration (TSA) Pre-Check program, because prevented companies are given a higher level of trust after meeting certain rigorous certification cri- teria. Several participants, including major consumer electronic companies, have already been enrolled in an early pilot version of this program. The participants will provide the FDA access to the measures they use to develop, test, and maintain software products, including ways that they collect post-market data.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:1–3
  • “To understand the strategic options for a vendor in bringing an AI product into the market and complying with regulations, it is important to review the regulatory process outside the United States. The European equivalent of the FDA is the Conformité Européenne (CE). The FDA typically requires evidence of both the safety and efficacy of a device, whereas a European CE mark requires only proof of safety and proof that the device performs consistently with the intend-ed use expressed by the manufacturer. Understandably, it is easier to obtain a CE mark than FDA approval. It is for this rea- son that some companies launch their prod- uct outside of the United States.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:1–3
  • “Although AI algorithms pose a unique challenge to medical regulation agencies, these challenges are being acknowledged and addressed by the FDA, which recognizes that the standards by which medical technology is evaluated may not apply to AI. By creating novel regulatory pathways, the FDA is encouraging the adoption of AI in medicine. The exact regulatory pathway and bur- den will be determined by intent—that is, whether AI is used for detection or diagnosis and whether is it used as an adjunct or a replacement. Regulatory standards are likely to evolve as AI algorithms become more robust and widespread.”
    Concepts in U.S. Food and Drug Administration Regulation of Artificial Intelligence for Medical Imaging
    Kohli A et al.
    AJR 2019; 213:1–3
  • “The FDA has not issued rules about test datasets, transparency, or verification proce- dures. It will probably evaluate models and associated test datasets on a case by case ba- sis. How this will evolve is unclear at present. In addition, regulation that created the FDA was enacted before the availability of ML, and existing laws regarding devices are difficult to apply to ML algorithms.”


    Implementing Machine Learning in Radiology Practice and Research 
Kohli M et al.
AJR 2017; 208:754–760
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