Collaborating Authors

Government Relations & Public Policy

Report: Most FDA-approved AI devices not effectively evaluated for fairness, accuracy


Most artificial intelligence-powered medical devices approved by the FDA have not been comprehensively studied to determine the fairness and …

The Ethics of AI In Healthcare


Father Paolo Benanti is an expert in ethics, digital ethics, and technology. He is a Franciscan monk and Professor of Moral Theology, Bioethics, and Neuroethics at the Gregorian Pontifical University in Rome. I discuss with Father Benanti the controversial aspects of AI in healthcare and how the digital transformation changes us – human beings. Father Benanti, two years ago, there was a morally ambiguous case in the USA – a doctor used a virtual presence system to tell a patient he would die. With the broad adoption of telemedicine and medical workforce shortages, this practice may become an everyday reality. From the beginning of human history, we have understood medicine as a scientific discipline. There was a time when a priest and doctor was the same person. We've always picked up someone special from the human community to hold the position of a doctor.

Edge Intelligence for Empowering IoT-based Healthcare Systems Artificial Intelligence

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.

FDA grants emergency authorization to 'machine learning-based' COVID detection device


The Food and Drug Administration this week gave emergency-use authorization to a "machine learning-based" device that will reportedly work to detect COVID even in cases in which no immediate symptoms are evident. The device, manufactured by Tiger Tech Solutions, "identifies certain biomarkers that may be indicative of SARS-CoV-2 infection … in asymptomatic individuals over the age of 5," the FDA said in a press release. The device works by reading signals of a patient's blood flow using an armband. "The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes," the FDA said. "Once the measurement is completed," the statement continued.

Justice, diversity, and research ethics review


The disproportionate impact of COVID-19 on certain populations, such as Black, Latinx, and Indigenous populations in the United States, has focused attention on inequalities in health and on the need to increase enrollment of racial and ethnic minorities and other underrepresented groups in biomedical research ([ 1 ][1]). Yet too often, in the United States and globally, participant enrollment in research has not reflected the demographic composition of the general population, those affected by the health conditions being studied, or those for whom the investigational product is intended ([ 2 ][2]), with racial and ethnic minorities and the young and the elderly, among others, being consistently underrepresented ([ 3 ][3]). Underlying causes for this underrepresentation have been described ([ 4 ][4], [ 5 ][5]), but change has been slow. Notwithstanding the roles of other stakeholders in addressing this issue, we maintain that the specific value of institutional review boards (IRBs) and research ethics committees (RECs) in promoting diversity has been underrecognized and their authority underutilized. Here, we substantiate the role of and outline practical steps for the IRB and REC (hereafter “IRB”) to help achieve greater diversity in clinical research. The appropriate inclusion of diverse populations in clinical research is necessary if we are to understand how biological variability and social determinants of health contribute to disease prevalence, transmission, course, experience of illness, and treatment outcome. The inclusion of understudied and underserved groups informs clinical decision-making and health policy and can serve efforts to address mistrust of research and health care ([ 6 ][6], [ 7 ][7]). Responsibility to the goals of diversity lies with all stakeholders in the clinical research enterprise ([ 6 ][6]), and a commitment to diversity, individually and collaboratively, by research sponsors, funders, academic institutions, contract research organizations, study sites, investigators, and IRBs is necessary. Most regulated clinical research undergoes obligate review and approval by an IRB. IRBs are charged with safeguarding the rights and well-being of human participants in accordance with the foundational tenets of respect for persons, beneficence, and justice, as described in the Belmont Report ([ 8 ][8]). An IRB's ethical responsibilities with regard to diversity derive from these and other principles, guidelines, and standards ([ 9 ][9], [ 10 ][10]). The discussion of justice in Belmont cites “moral requirements that there be fair procedures and outcomes in the selection of research subjects.” As Belmont and other codes of ethics emerged from a historical backdrop of abuse and injustice in research, “fair procedures” have been applied by IRBs largely (and, we believe, too narrowly) to ensure that subjects are not exploited and enrolled as a matter of convenience. The idea of justice within the Belmont Report also includes the notion of access to the benefits of research (i.e., knowledge gained); this has direct implications for populations that have been understudied, whether incidentally or systematically. Subject selection cannot be equitable, and the requirements of justice cannot be met, when there is de facto exclusion of understudied populations. This notion of justice is supported by the World Health Organization's International Ethical Guidelines for Health-related Research Involving Humans, Guideline 3, which states, “In cases where the underrepresentation of particular groups results in or perpetuates health disparities, equity may require special efforts to include members of those populations in research” ([ 9 ][9]), and by the World Medical Association Declaration of Helsinki, which states, “Groups that are underrepresented in medical research should be provided appropriate access to participation in research” ([ 10 ][10]). Therefore, consideration of diversity is essential to the question of fairness in subject selection and to IRB review. Diversity in clinical research is responsive to the principle of beneficence, which places priority on the welfare of research participants and creates the obligation that research presents a favorable balance of benefit to risk, after risks and burdens have been minimized. In calling for “maximization of benefits” in the research, Belmont directs attention to both individual benefit and to the broader value of research to society. A clinical research enterprise that is not inclusive does not adequately address the health needs of a diverse society. Group differences in susceptibility to disease and in treatment outcome can only be identified when those groups are studied. It is the obligation of an IRB to maximize benefits through the inclusion of understudied groups in a manner that is consistent with the study aims and does not introduce unacceptable harm or burden. Belmont describes two ethical convictions in relation to respect for persons, self-determination, and decision-making: the obligations to treat individuals as autonomous agents and to protect those with diminished autonomy. IRBs provide additional safeguards for research involving participants with compromised voluntariness (e.g., prisoners) or impaired comprehension. With regard to the inclusion of diverse populations, respect for persons demands efforts to foster informed and autonomous decision-making and, therefore, to address common barriers posed by age, language, culture, and educational disadvantage. Respect for persons requires the identification of opportunities and resources to engage understudied populations and to enhance awareness, access, and inclusion in research ([ 4 ][4], [ 6 ][6]). It also demands modification of those aspects of research and of consent that inadvertently limit the participation of understudied populations. For example, although inclusion of non-English speakers in a study may involve additional expenses of translation and/or interpreters, it strengthens the commitment to autonomy and justice. The ethical positions presented above compel attention to inclusion of diverse populations in clinical research and define a specific duty for the IRB. In a 2019 survey ([ 11 ][11]), a majority of IRB chairs, IRB administrators, and investigators agreed that “IRBs should play a key role in ensuring diversity among participants in terms of gender, ethnicity, and language.” Despite this, there has been scant regulatory consideration, and little formal discussion within the field, as to whether diversity falls within the IRB's remit. There are also little data as to whether and when IRBs exercise this authority, but the observed underrepresentation in completed studies suggests that IRBs do not consistently attend to this responsibility. Further questions relate to recent U.S. regulation and policy requiring a single, designated IRB to serve as the IRB of record for multicenter research and whether this will offer benefit in consistency and reach with regard to diversity and inclusion. In the face of the persistent problem of underrepresentation in clinical research, institutions should establish policies and provide necessary resources at all institutional levels to ensure that reviewing IRBs fulfill this obligation. The specific approaches we outline here will serve to help incorporate the ethical oversight of diversity in IRB procedures, deliberations, and expectations (see the box). An IRB has authority to require that a research protocol details study elements relevant to considerations of diversity. A description and justification by the investigator of the demographics of the intended study sample (e.g., by age, sex, race, ethnicity, social determinants of health) and a description of either the demographics of the condition or those using or intended to use the product in the general population permit the IRB to make an assessment of the appropriateness of the recruitment plan. When the makeup of the proposed sample deviates substantially from that of the demographics of the condition being studied in the general population or for whom the intervention is intended, and no valid scientific justification is offered, the IRB can require modification of the study to recruit a more representative sample. Such requirements are tailored to the nature and phase of the study, the study's specific aims, and the study location, as discussed further below. #### Institutional review board oversight: Points to consider by reviewers ##### Initial Review ###### Study aims and subject selection ###### Criteria for inclusion and exclusion ###### Recruitment ###### Study conduct ###### Payment ###### Return of results ##### Continuing Review Note that inclusion of a demographically diverse study population does not imply that statistical conclusions regarding heterogeneity of treatment outcome will be possible, but it may allow directional assessments of efficacy and safety that can then be further investigated. Inclusion will, at a minimum, address the equitable selection of participants and the principle of justice in research. During review, an IRB should consider the feasibility of study methods that seek to identify, recruit, and retain underrepresented populations. Research team partnerships with patients and their families, advocacy groups, and community representatives have been shown to be effective in informing recruitment and retention strategies ([ 12 ][12]) as well as in providing input on study questions and participant-relevant endpoints, study conduct, and culturally and linguistically appropriate communications. The IRB should require a statement in the study proposal summarizing the nature, process, input, and impact of such patient and community engagement and how this information has shaped the study itself and the recruitment plan; simply asking the question will prompt consideration by investigators. The IRB can review and provide specific feedback to facilitate successful recruitment of specific populations, including language use, translation, placement of advertisements, and workforce characteristics. The IRB should also ensure that all study materials adhere to health literacy principles and that user-testing is utilized where indicated. The IRB should identify factors, such as excessive time commitment, restricted clinic hours, the costs of travel, and inadequate compensation, that have a foreseeable and negative impact on the enrollment of an appropriately representative sample ([ 13 ][13]). IRBs should require investigators to detail study inclusion and exclusion criteria and, when not self-evident, to provide a rationale for exclusion. Review of eligibility criteria should ensure that understudied populations are not inadvertently or unnecessarily excluded and that criteria are only as restrictive as necessary for safety and to minimize harm. For example, the exclusion of older populations with a specific age criterion might be revised to exclude individuals with specific health concerns who would be at increased risk, regardless of age. When laboratory measures serve as the basis for eligibility criteria, they should be adapted to reflect known sex-, age-, race-, or ancestry-specific normal values, when failure to do so would unnecessarily decrease eligibility of some individuals ([ 14 ][14]). IRBs should identify common practices that limit enrollment of immigrant or minority language speakers in multilingual communities, restrict the participation of women of child-bearing potential (when requiring appropriate contraception would suffice), and introduce bias in participant selection by using overly subjective criteria (such as “investigator discretion”). In exercising the regulatory requirement for continuing oversight of ongoing research, the IRB should periodically review the demographic breakdown of the accrued sample by age, race, ethnicity, sex, and social determinants of health where applicable to the research. Along with these data, IRBs should require an explanation of any meaningful departures from the recruitment plan and request, review, and approve proposed corrective action when indicated. Ongoing tracking of accrual by the IRB, as well as dialogue between the IRB and investigator, communicates the importance of diversity, promotes transparency with regard to progress or lack of progress, provides a measure of accountability, and, ultimately, will change behavior. IRB requirements with regard to study demographics should be flexibly tailored to individual study purpose, phase, setting, and size. For example, for some research (e.g., phase 3 studies, comparative effectiveness research), an IRB may adopt the principle, as a rebuttable presumption, that a study sample should reflect the demographic makeup of the condition being studied or for whom the intervention is intended. Other studies, such as small exploratory, proof-of-concept, early phase studies, or research that seeks to learn about specific communities, would not be expected to be representative of those affected by the condition. Similarly, a local site in a multisite study may be selected because it proposes to recruit a specific racial or ethnic group to diversify a larger study population. Equitable subject selection requires the balancing of inclusion and protection and, like all aspects of research with human participants, grounding in good science. When a study proposes to recruit a sample that is composed predominantly or solely of a racial, ethnic, or other minority, the IRB might reasonably ask why the selection of this sample is scientifically necessary, how the findings are generalizable, and whether an alternative recruitment strategy might yield a more diverse or less burdened, stigmatized, or disadvantaged population. Flexibly adapting requirements to specific study types will encourage dialogue between investigators and the IRB. When an investigator faces particular challenges in the recruitment and retention of specific populations, the IRB could offer guidance or consultation on protocol revision. The IRB itself should be diverse in composition, with membership and input reflecting the demographic compositions of the communities and populations studied in the research it reviews through the use of ad hoc consultants and by appointing members with experience working with diverse communities. However, a recent study showed that 87% of IRB members and 91% of IRB chairs were white ([ 11 ][11]). A diverse IRB will be better attuned to the experience and needs of participants and better able to offer input from the perspective of varied populations. At a minimum, training in cultural competence and implicit bias should become part of required ethics education for all IRB members and staff. Finally, IRBs should develop expertise in providing concrete recommendations for investigators in methods and tools to achieve greater diversity ([ 6 ][6]). Impediments to inclusion of underrepresented and underserved populations in research are numerous and complex. There are no specific regulatory mandates of the kind that typically drive accountability in clinical research. Institutional commitment to diversity is uneven, the research workforce itself is inadequately diverse, and resistance from the research community to any additional oversight is likely. Further, expertise in the engagement and study of hard-to-reach populations is variable and related infrastructure is limited. Finally, in the United States, some question whether Belmont or the Common Rule are appropriately applied to matters of social justice. Institutions should support, educate, and resource IRBs, investigators and their study teams, and others in research so that they can give necessary attention to diversity as a fundamental value in the ethical conduct of research. The application of diversity to research review is neither simple nor without risk, but we do not believe the requirement fundamentally differs from other components of IRB review. Overly prescriptive approaches by the IRB and REC, specific mandates, or the application of quotas to study samples will not serve the interests of science and would not be justifiable or palatable to the research community. Drawing attention to diversity and inclusion as a goal and setting reasonable expectations as a condition of study approval, however, will give rise to necessary discussion and collaboration between IRBs and among investigators and the evolution of best practices in the field. Of course, the obligation to promote diversity in clinical research does not rest solely on the IRB or REC or the investigators. Sponsors, regulators, research and academic institutions, funders, patients and patient advocates, and others must build capacity and infrastructure in what, in the end, must be a collaborative enterprise. As entities that hold investigators accountable, IRBs are themselves accountable to their ethical and regulatory mandates and ultimately to those who serve as participants in research. The duty of IRBs to view subject enrollment and retention beyond the lens of “protection,” to deliberate on the benefits and risks of greater inclusion, and to exercise their authority to promote diversity should be recognized and actively implemented as a matter of justice. 1. [↵][15]1. D. B. Chastain et al ., N. Engl. J. Med. 383, e59 (2020). [OpenUrl][16][PubMed][17] 2. [↵][18]1. T. C. Knepper, 2. H. L. McLeod , Nature 557, 157 (2018). [OpenUrl][19][CrossRef][20] 3. [↵][21]U.S. Food and Drug Administration (FDA), “2015-2019 drug trials snapshots summary report: Five-year summary and analysis of clinical trial participation and demographics” (FDA, 2020); [][22]. 4. [↵][23]1. S. George, 2. N. Duran, 3. K. Norris , Am. J. Public Health 104, e16 (2014). [OpenUrl][24][CrossRef][25][PubMed][26] 5. [↵][27]1. L. T. Clark et al ., Curr. Probl. Cardiol. 44, 148 (2019). [OpenUrl][28] 6. [↵][29]1. B. E. Bierer et al ., “Achieving diversity, inclusion, and equity in clinical research,” version 1.0 (Multi-Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard, 2020); . 7. [↵][30]1. R. C. Warren, 2. M. G. Shedlin, 3. E. Alema-Mensah, 4. C. Obasaju, 5. D. A. Hodge Sr. , Ethics Med. Public Health 10, 128 (2019). [OpenUrl][31] 8. [↵][32]The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, “The Belmont Report: Ethical principles and guidelines for the protection of human subjects of research” (U.S. Department of Health, Education, and Welfare, 1979); [][33]. 9. [↵][34]World Health Organization, Council for International Organizations of Medical Sciences (CIOMS), “International ethical guidelines for health-related research involving humans” (CIOMS, ed. 4, 2016); . 10. [↵][35]World Medical Association, “WMA declaration of Helsinki—Ethical principles for medical research involving human subjects,” 9 July 2018; [][36]. 11. [↵][37]1. S. H. Berry et al ., “Profile of institutional review board characteristics prior to the 2019 implementation of the revised common rule” (RAND Coproration, 2019); [][38]. 12. [↵][39]1. T. Greenhalgh et al ., Health Expect. 22, 785 (2019). [OpenUrl][40][CrossRef][41][PubMed][42] 13. [↵][43]1. L. Gelinas et al ., N. Engl. J. Med. 378, 766 (2018). [OpenUrl][44][CrossRef][45][PubMed][17] 14. [↵][46]1. D. A. Vyas, 2. L. G. Eisenstein, 3. D. S. Jones , N. Engl. J. Med. 383, 874 (2020). [OpenUrl][47][CrossRef][48][PubMed][17] Acknowledgments: D.H.S. received consulting fees as a member of the Takeda Pharmaceuticals Bioethics Advisory Council. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: #xref-ref-1-1 "View reference 1 in text" [16]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D383%26rft.spage%253De59%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: /lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fsci%2F371%2F6535%2F1209.atom [18]: #xref-ref-2-1 "View reference 2 in text" [19]: {openurl}?query=rft.jtitle%253DNature%26rft.volume%253D557%26rft.spage%253D157%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fd41586-018-05049-5%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/external-ref?access_num=10.1038/d41586-018-05049-5&link_type=DOI [21]: #xref-ref-3-1 "View reference 3 in text" [22]: [23]: #xref-ref-4-1 "View reference 4 in text" [24]: {openurl}?query=rft.jtitle%253DAm.%2BJ.%2BPublic%2BHealth%26rft.volume%253D104%26rft.spage%253De16%26rft_id%253Dinfo%253Adoi%252F10.2105%252FAJPH.2013.301706%26rft_id%253Dinfo%253Apmid%252F24328648%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: /lookup/external-ref?access_num=10.2105/AJPH.2013.301706&link_type=DOI [26]: /lookup/external-ref?access_num=24328648&link_type=MED&atom=%2Fsci%2F371%2F6535%2F1209.atom [27]: #xref-ref-5-1 "View reference 5 in text" [28]: {openurl}?query=rft.jtitle%253DCurr.%2BProbl.%2BCardiol.%26rft.volume%253D44%26rft.spage%253D148%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [29]: #xref-ref-6-1 "View reference 6 in text" [30]: #xref-ref-7-1 "View reference 7 in text" [31]: {openurl}?query=rft.jtitle%253DEthics%2BMed.%2BPublic%2BHealth%26rft.volume%253D10%26rft.spage%253D128%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [32]: #xref-ref-8-1 "View reference 8 in text" [33]: [34]: #xref-ref-9-1 "View reference 9 in text" [35]: #xref-ref-10-1 "View reference 10 in text" [36]: [37]: #xref-ref-11-1 "View reference 11 in text" [38]: [39]: #xref-ref-12-1 "View reference 12 in text" [40]: {openurl}?query=rft.jtitle%253DHealth%2BExpect.%26rft.volume%253D22%26rft.spage%253D785%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fhex.12888%26rft_id%253Dinfo%253Apmid%252F31012259%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: /lookup/external-ref?access_num=10.1111/hex.12888&link_type=DOI [42]: /lookup/external-ref?access_num=31012259&link_type=MED&atom=%2Fsci%2F371%2F6535%2F1209.atom [43]: #xref-ref-13-1 "View reference 13 in text" [44]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D378%26rft.spage%253D766%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMsb1710591%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [45]: /lookup/external-ref?access_num=10.1056/NEJMsb1710591&link_type=DOI [46]: #xref-ref-14-1 "View reference 14 in text" [47]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D383%26rft.spage%253D874%26rft_id%253Dinfo%253Adoi%252F10.1056%252Fnejmms2004740%26rft_id%253Dinfo%253Apmid%252Fhttp%253A%252F%252Fwww.n%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [48]: /lookup/external-ref?access_num=10.1056/nejmms2004740&link_type=DOI

Imbio's New Cardiothoracic Imaging Algorithm Receives FDA 510(k) Approval


Imbio has gained FDA 510(k) clearance for its RV/LV AnalysisTM algorithm, a leading supplier of artificial intelligence (AI) solutions for medical imaging evaluation. The RV/LV Analysis algorithm is a quick and easy way to check for right ventricular dilation. The tool efficiently and precisely evaluates the heart's ventricles to calculate the proportion of the right to left ventricle's maximum diameter. The RV/LV Analysis results are readily accessible for clinicians without any extra work, including a detailed report of quantitative findings directly attached to the patient imaging study in minutes. David Hannes, Imbio Chief Executive Officer, stated that their automated RV/LV Assessment has the control to supply factual information and notify risk stratification in many acute cases. Imbio is proud to offer this AI-driven algorithm to physicians and partners to support acute cases and facilitate critical treatment decisions for patients.

FDA-approved gaming is already here, pointing to its therapeutic potential

Washington Post - Technology News

In analyzing the news and media landscape, the report states that the nature of subscription services has changed, and will continue to evolve as consumers will be asked to pay for virtual fashion, experiences and games. The report cites subscription service platform Zuora that the media business has an average subscription dropout rate of 34 percent, the highest of any industry sector studied. It stresses that local newspapers are not just competing with the likes of The Washington Post or The New York Times, but every audience-funded business.

Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases) Artificial Intelligence

Diagnosis of chronic diseases and assistance in medical decisions is based on machine learning algorithms. In this paper, we review the classification algorithms used in the health care system (chronic diseases) and present the neural network-based Ensemble learning method. We briefly describe the commonly used algorithms and describe their critical properties. Materials and Methods: In this study, modern classification algorithms used in healthcare, examine the principles of these methods and guidelines, and to accurately diagnose and predict chronic diseases, superior machine learning algorithms with the neural network-based ensemble learning Is used. To do this, we use experimental data, real data on chronic patients (diabetes, heart, cancer) available on the UCI site. Results: We found that group algorithms designed to diagnose chronic diseases can be more effective than baseline algorithms. It also identifies several challenges to further advancing the classification of machine learning in the diagnosis of chronic diseases. Conclusion: The results show the high performance of the neural network-based Ensemble learning approach for the diagnosis and prediction of chronic diseases, which in this study reached 98.5, 99, and 100% accuracy, respectively.

Is Clover Health Stock a Buy?


The company sells Medicare Advantage plans, focusing on customer experience and leveraging machine learning and artificial intelligence to …