Preventing Delayed and Missed Care by Applying Artificial Intelligence to Trigger Radiology Imaging Follow-up 
Jane Domingo et al.
NEJM Catalyst Vol. 3 No. 4 April 2022 DOI: 10.1056/CAT.21.0469

“Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.”
Radiomic Analysis: Study Design, Statistical Analysis,and Other Bias Mitigation Strategies
Chaya S. Moskowitz et al.
Radiology 2022; (in press)

”Although software packages to implement analyses arereadily available and increasingly user friendly, if they are notimplemented with the necessary expertise or correct guidance,there is a high risk that incorrect conclusions will bedrawn from the work. The field of radiomics lies at the intersection of medicine, computer science, and statistics.We contend that to produce clinically meaningful results thatpositively impact patient care and minimize biases and pitfalls,radiomic analysis requires a multidisciplinary approachwith a research team that includes individuals with multipleareas of expertise.”
Radiomic Analysis: Study Design, Statistical Analysis,and Other Bias Mitigation Strategies
Chaya S. Moskowitz et al.
Radiology 2022; (in press)

Radiomic Analysis: Study Design, Statistical Analysis,and Other Bias Mitigation Strategies
Chaya S. Moskowitz et al.
Radiology 2022; (in press)

“The use of artificial intelligence (AI) has grown dramatically in the past few years in the United States and worldwide, with more than 300 AI-enabled devices approved by the U.S. Food and Drug Administration (FDA). Most of these AI-enabled applications focus on helping radiologists with detection, triage, and prioritization of tasks by using data from a single point, but clinical practice often encompasses a dynamic scenario wherein physicians make decisions on the basis of longitudinal information. Unfortunately, benchmark data sets incorporating clinical and radiologic data from several points are scarce, and, therefore, the machine learning community has not focused on developing methods and architectures suitable for these tasks. Current AI algorithms are not suited to tackle key image interpretation tasks that require comparisons to previous examinations. Focusing on the curation of data sets and algorithm development that allow for comparisons at different points will be required to advance the range of relevant tasks covered by future AI-enabled FDA-cleared devices.”
The Need for Medical Artificial Intelligence That Incorporates Prior Images 
Julián N. Acosta et al. 
Radiology 2022; 000:1–6 • https://doi.org/10.1148/radiol.212830

“Most of these AI-enabled applications focus on helping radiologists with detection, triage, and prioritization of tasks by using data from a single point, but clinical practice often encompasses a dynamic scenario wherein physicians make decisions on the basis of longitudinal information. Unfortunately, benchmark data sets incorporating clinical and radiologic data from several points are scarce, and, therefore, the machine learning community has not focused on developing methods and architectures suitable for these tasks. Current AI algorithms are not suited to tackle key image interpretation tasks that require comparisons to previous examinations.”
The Need for Medical Artificial Intelligence That Incorporates Prior Images 
Julián N. Acosta et al. 
Radiology 2022; 000:1–6 • https://doi.org/10.1148/radiol.212830

“In summary, even though physicians routinely perform comparisons with prior examinations when interpreting images in clinical practice, only a few artificial intelligence (AI) algorithms currently available are able to incorporate information from more than one point to help in these critical tasks. The curation of high-quality data sets with longitudinal clinical and imaging data, and the development of AI algorithms capable of solving a wider range of problems, will be essential to provide meaningful improvements in clinical workflows.”
The Need for Medical Artificial Intelligence That Incorporates Prior Images 
Julián N. Acosta et al.
Radiology 2022; 000:1–6 • https://doi.org/10.1148/radiol.212830

”AI liability insurance would reduce the liability risk to developers, physicians, and hospitals. Insurance is a tool for managing risk, allowing the insurance policy holders to benefit from pooling risk with others. Insurance providers are intermediaries that play an organizing role in creating these pools and performing actuarial assessment of associated risks. While many types of insurance exist in the health care context, our focus in this article is entirely on AI liability insurance rather than coverage for health care services.”
AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care 
Ariel Dora Stern et al. 
NEJM Catalyst Vol. 3 No. 4 April 2022 DOI: 10.1056/CAT.21.0242

“Developers of health care AI products face the risk of product liability lawsuits when their products injure patients, whether injuries arise from defective manufacturing, defective design, or failure to warn users about mitigable dangers.16 Physicians may also face risks from patient injuries stemming from the use of AI, including faulty recommendations or inadequate monitoring. Similarly, hospitals or health systems may face liability as coordinating providers of health care or on the basis of inadequate care in supplying AI tools — an analogy to familiar forms of medical liability for providing inadequate facilities or negligently credentialing a physician practicing at the hospital. Such risks may reduce incentives to adopt AI tools.”
AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care 
Ariel Dora Stern et al. 
NEJM Catalyst Vol. 3 No. 4 April 2022 DOI: 10.1056/CAT.21.0242

”AI liability insurance would reduce the liability risk to developers, physicians, and hospitals. Insurance is a tool for managing risk, allowing the insurance policy holders to benefit from pooling risk with others. Insurance providers are intermediaries that play an organizing role in creating these pools and performing actuarial assessment of associated risks. While many types of insurance exist in the health care context, our focus in this article is entirely on AI liability insurance rather than coverage for health care services.”
AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care 
Ariel Dora Stern et al. 
NEJM Catalyst Vol. 3 No. 4 April 2022 DOI: 10.1056/CAT.21.0242

” The credentialing function of insurance will thus reinforce the patient-centered incentives of AI developers Consequently, this insurance may alleviate health care provider concerns, at least to the point at which they are willing to adopt the AI technology. Indeed, this should be the case regardless of whether the AI manufacturer or the health care provider is the holder of the insurance policy, as long as such a policy can be purchased. However, the price and implicit value of insurance are likely to be passed through. For example, a manufacturer selling an AI tool that comes with liability insurance will be able to command a higher price than for the same tool without such insurance. Insurers may also require ongoing performance data from AI developers, whether they are in house or commercial; such data could be well beyond those needed to meet the requirements of regulatory premarket review.28 While insurers do not provide the same level of centralized review that regulators do, they may well serve a more context-sensitive, hands-on evaluative role focused on both quantifying and reducing risk — a role that may be especially important given the questionable generalizability of many current-generation AI systems.”
AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care 
Ariel Dora Stern et al. 
NEJM Catalyst Vol. 3 No. 4 April 2022 DOI: 10.1056/CAT.21.0242

“Proponents of artificial intelligence (“AI”) technology have suggested that in the near future, AI software may replace human radiologists. While AI’s assimilation into the specialty has occurred more slowly than predicted, developments in machine learning, deep learning, and neural networks suggest that technological hurdles and costs will eventually be overcome. However, beyond these technological hurdles, formidable legal hurdles threaten AI’s impact on the specialty. Legal liability for errors committed by AI will influence AI’s ultimate role within radiology and whether AI remains a simple decision support tool or develops into an autonomous member of the healthcare team.”
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
https://doi.org/10.2214/AJR.21.27224

“Additional areas of uncertainty include the potential application of products liability law to AI, and the approach taken by the U.S. FDA in potentially classifying autonomous AI as a medical device. The current ambiguity of the legal treatment of AI will profoundly impact autonomous AI development given that vendors, radiologists, and hospitals will be unable to reliably assess their liability from implementing such tools. Advocates of AI in radiology and health care in general should lobby for legislative action to better clarify the liability risks of AI in a way that does not deter technological development.”
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

”Duplicating radiologists’ abilities through technology has proven more of a challenge than originally posited, with resultant skepticism regarding AI’s ultimate impact on the field, at least for the near term. Technological hurdles and costs will fall, and it is only a matter of time until machines can offer a reasonable facsimile of the radiologist report. However, even beyond these technological hurdles, formidable legal obstacles, often not given enough attention in the literature, threaten AI’s impact on the specialty and, if unchanged, have the potential to preclude the future success of this emerging industry.”
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

“Fundamentally, the legal handling of AI will hinge on the degree of autonomy exercised by the AI software. If the primary use of AI is simply as a decision support tool to highlight findings for the radiologist, who thereafter makes the final determinations and issues a report, the issues are quite simple. The radiologist who makes the final determination bears the liability risk.”
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

”A radiologist breaches this duty when the expected standard of care is not met. The standard of care is the degree of care that a “reasonably prudent radiologist” would be expected to exercise under the same or similar circumstances. The issue of liability is one of reasonableness: what would a reasonably prudent radiologist do in this situation? This standard of care will largely be established in the context of the courtroom using expert witness testimony, whereby other radiologists opine as to what, in their professional opinion, would be a reasonable action in this situation.” 
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

”But how does medical malpractice even work in the setting of an autonomous algorithm? Is there a similar physician-patient relationship when the “physician” is an algorithm? How is an AI algorithm held to the “reasonably prudent radiologist” (or perhaps “reasonably prudent algorithm”) standard, and who could serve as expert witness to determine this standard? Is there a different standard of care or expectation for an algorithm, and does the expectation change if the algorithm is performing tasks that go beyond the capabilities of the typical human radiologist (e.g., predicting optimum therapy options or responses based on imaging or genomic lesion characterization)? Ultimately, the facility hosting the AI likely would bear liability, and malpractice principles would no longer be applicable or even defensible; the circumstance would essentially become a form of “enterprise” liability.”
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

”An injured patient tends to be a sympathetic witness in the eyes of a jury, whereas an AI algorithm would be unsympathetic; faceless emotionless robots make for very bad defendants. A skilled plaintiff’s attorney would elicit a mental image of machines running amok, including cold passionless robots making life and death judgments; jurors, inclined to fear technology from a lifetime of science fiction dystopias, would likely “throw the book” at the defendant. The idea that a medical center would replace a caring and compassionate doctor with a robot such as HAL 9000 to maximize revenue would not play well to a jury.”
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

“Debate is ongoing regarding the appropriate integration of AI tools with human decision- makers (including non-radiologists), the risks of ignoring AI outputs as AI use becomes he standard of care, and potential issues in overreliance on AI tools that may be relevant to liability. AI law remains in its early stages, and ongoing uncertainty is present regarding the manner in which courts will allocate liability for AI mistakes in radiology and the impact that such costs may have on AI development. Proponents of AI should recognize the legal system’s complexities and hurdles.” 
Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy 
Jonathan L. Mezrich, MD, JD, LLM, MBA 
AJR 2022 Feb 9 [published online]. Accepted manuscript. doi:10.2214/AJR.21.27224

“The proliferation of AI also raises concerns around accountabil- ity, as it is currently unclear whether developers, regulators, sellers or healthcare providers should be held accountable if a model makes mistakes even after being thoroughly clinically validated. Currently, doctors are held liable when they deviate from the standard of care and patient injury occurs. If doctors are generally skeptical of medical AI, then individual doctors may be adversely influenced to ignore AI recommendations that conflict with standard practice, even if those recommendations may be personalized and beneficial for a specific patient. However, if the standard of care shifts so that doctors routinely use AI tools,then there will be a strong medicolegal incentive for doctors to follow AI recommendations.”
AI in health and medicine
Pranav Rajpurkar, Emma Chen, Oishi Banerjee and Eric J. Topol 
NATURE MEDICINE VOL 28 January 2022 31–38

“The growing use of artificial intelligence (AI) in health care has raised questions about who should be held liable for medical errors that result from care delivered jointly by physicians and algorithms. In this survey study comparing views of physicians and the U.S. public, we find that the public is significantly more likely to believe that physicians should be held responsible when an error occurs during care delivered with medical AI, though the majority of both physicians and the public hold this view (66.0% vs 57.3%; P = .020). Physicians are more likely than the public to believe that vendors (43.8% vs 32.9%; P = .004) and healthcare organizations should be liable for AI-related medical errors (29.2% vs 22.6%; P = .05). Views of medical liability did not differ by clinical specialty. Among the general public, younger people are more likely to hold nearly all parties liable.”
Public vs physician views of liability for artificial intelligence in health care. 
Khullar D et al. 
J Am Med Inform Assoc. 2021 Jul 14;28(7):1574-1577

A. yes
B. no
C. maybe

“One must ask, then, what is the burden of value a radiology AI product must provide to justify purchase? The answer depends on many factors including the health care setting and its purchasing structure, the health care payer system, and patient distribution, but a nearly universal thread is that the software must provide financial return on investment. The challenge is matching the “return” to the “investor.” When these two parties are mismatched, the cost justification for one group’s investment for another group’s benefit rarely occurs.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

“If an AI model can increase the throughput for ex- amination interpretation, the practice can now absorb this additional volume without an additional radiolo- gist hire. Similarly, models that triage low complexity or negative studies can be used to route these exami- nations to physician extenders and reduce costs for a group by over 75% while maintaining imaging revenue. Conversely, AI models that close the loop on patient follow-up or detect incidental findings have no financial benefit for a private practice and therefore are less likely to be paid for by the radiology group.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

”Similarly, driving additional outpatient referrals into the radiology department (ie, additional examinations) can also be significant revenue generators. For example, closing the loop on an incidental adrenal nodule can result in an additional triple-phase CT or MRI examinations and thousands of dollars in additional revenue while providing standard of care for the patient. Models that provide opportunistic screening such as scoring of coronary artery calcium on routine nongated chest CT can identify high-risk patients for cardiology referral, some of whom may ultimately receive advanced interventions. Capture or retention of a patient into the health system provides significant revenue streams, and in each of these cases the financial incentive and champions for adoption of these models are outside of the radiology department. Ultimately, these models have little impact on radiology workflow or the finances of a radiology practice.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

”In the emergency setting in which community hospitals may not have 24-7 radiologist coverage, AI models can help increase patient throughput and lead to significant cost savings. Many emergency pro- viders must currently choose the lesser of two evils—have patients wait overnight for examination re- ports or independently interpret examinations to guide patient disposition. Deployment of AI models in these settings can increase confidence in discharging patients for negative examinations or help quickly flag emergent findings that require immediate intervention.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

”Nevertheless, at least two companies have recently secured additional CPT codes for use of AI software—one for detection of vertebral compression fracture on CT (www.zebramedical.com) and another for scoring trabecular bone health on bone densitometry examinations (www.nanox.vision) to improve risk stratification for osteopenic and osteoporotic patients. However, it is important to note that reimbursements for AI software may have unintended consequences on the reimbursement for radiology examination interpretation, particularly for cases in which AI software reduces the average interpretation time of the examination.” 
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

”Lastly, although the majority of care in the United States is based on fee-for-service, there are a few domestic (eg, Veterans Affairs Hospitals) and many more international examples of vertical payment models in which there are incentives to improve quality as a means to reduce cost. In these set- tings, a win-win-win is possible for patients, payers, and physicians. For example, in a fee-for-service system, a model that reduces unnecessary biopsies in screening mammography is good for patients and payers but may face barriers to adoption because it decreases hospital and practice revenue. However, the same model is a single-payer system is a win-win-win: patients receive better care, physicians have decreased workload, and payers significantly reduce costs. For this reason, many AI companies have seen wider adoption in Europe and Asia as compared with the United States.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

“In summary, the path to adoption of radiology AI is complex but must be viewed through a realistic lens that considers the economic truths of the health care system. Software that improves quality alone without a secondary benefit in efficiency, referrals, or another revenue stream is difficult to justify in a fee-for-service model, but these same models are being actively adopted within single-payer systems domestically and internationally. Commercial radiology AI vendors should consider these dynamics when developing models for various markets and tailor their value propositions to the needs of the potential customer. Ultimately, this will increase AI adoption and transform radiology AI into a financially sustainable tool for radiologists, hospitals systems, and most importantly patients.”
The Business of Artificial Intelligence in Radiology Has Little to Do With Radiologists 
Trivedi H
J Am Coll Radiol. 2022 Feb 15:S1546-1440(22)00113-2.

Introduction: Applications of artificial intelligence (AI) in health care have increased in the past decade, but little is known about how patients view these applications and whether they have concerns. We conducted a nationally representative survey to understand public perceptions of the use of AI in diagnosis and treatment.
Results: Comfort with AI varied by clinical application. For example, 12.3% of respondents were very comfortable and 42.7% were somewhat comfortable with AI reading chest radiographs, butonly 6.0% were very comfortable and 25.2%were somewhat comfortable about AI making cancer diagnoses. Most respondents were very concerned or somewhat concerned about AI’s unintended consequences, including misdiagnosis (91.5%), privacy breaches (70.8%), less time with clinicians(69.6%), and higher health care costs (68.4%). A higher proportion of respondents who self identified as being members of racial and ethnic minority groups indicated being very concerned about these issues, compared with White respondents.
Perspectives of Patients About Artificial Intelligence in Health Care
Dhruv Khullar et al.
JAMA Network Open. 2022;5(5):e2210309.

“Most respondents had positive views about AI’s ability to improve care but had concerns about its potential for misdiagnosis, privacy breaches, reducing time with clinicians, and increasing costs, with racial and ethnic minority groups expressing greater concern. Respondents were more comfortable with AI in specific clinical settings, and most wanted to know when AI was used in their care.”
Perspectives of Patients About Artificial Intelligence in Health Care
Dhruv Khullar et al.
JAMA Network Open. 2022;5(5):e2210309.