News & Comment

Filter By:

  • Over the past 7 years, regulatory agencies have approved hundreds of artificial intelligence (AI) devices for clinical use. In late 2020, payers began reimbursing clinicians and health systems for each use of select image-based AI devices. The experience with traditional medical devices has shown that per-use reimbursement may result in the overuse use of AI. We review current models of paying for AI in medicine and describe five alternative and complementary reimbursement approaches, including incentivizing outcomes instead of volume, utilizing advance market commitments and time-limited reimbursements for new AI applications, and rewarding interoperability and bias mitigation. As AI rapidly integrates into routine healthcare, careful design of payment for AI is essential for improving patient outcomes while maximizing cost-effectiveness and equity.

    • Ravi B. Parikh
    • Lorens A. Helmchen
    Comment Open Access
  • Healthcare is a large contributor to greenhouse gas (GHG) emissions around the world, given current power generation mix. Telemedicine, with its reduced travel for providers and patients, has been proposed to reduce emissions. Artificial intelligence (AI), and especially autonomous AI, where the medical decision is made without human oversight, has the potential to further reduce healthcare GHG emissions, but concerns have also been expressed about GHG emissions from digital technology, and AI training and inference. In a real-world example, we compared the marginal GHG contribution of an encounter performed by an autonomous AI to that of an in-person specialist encounter. Results show that an 80% reduction may be achievable, and we conclude that autonomous AI has the potential to reduce healthcare GHG emissions.

    • Risa M. Wolf
    • Michael D. Abramoff
    • Harold P. Lehmann
    Comment Open Access
  • Due to its enormous capacity for benefit, harm, and cost, health care is among the most tightly regulated industries in the world. But with the rise of smartphones, an explosion of direct-to-consumer mobile health applications has challenged the role of centralized gatekeepers. As interest in health apps continue to climb, national regulatory bodies have turned their attention toward strategies to protect consumers from apps that mine and sell health data, recommend unsafe practices, or simply do not work as advertised. To characterize the current state and outlook of these efforts, Essén and colleagues map the nascent landscape of national health app policies and raise several considerations for cross-border collaboration. Strategies to increase transparency, organize app marketplaces, and monitor existing apps are needed to ensure that the global wave of new digital health tools fulfills its promise to improve health at scale.

    • James A. Diao
    • Kaushik P. Venkatesh
    • Joseph C. Kvedar
    Editorial Open Access
  • As clinicians and scientists gather more data on the clinical trajectory of COVID-19 and the biology of its causative agent, the SARS-CoV-2 virus, novel strategies are needed to integrate these data to inform new therapies. A recent study by Howell et al. introduces a network model of viral-host interactions to produce explainable and testable predictions for treatment effects. Their model was consistent with experimental data and recommended treatments, and one of its predicted drug combinations was validated through in vitro assays. These findings support the utility of computational strategies for leveraging the vast literature on COVID-19 to generate insights for drug repurposing.

    • James A. Diao
    • Marium M. Raza
    • Joseph C. Kvedar
    Editorial Open Access
  • Health care is a human process that generates data from human lives, as well as the care they receive. Machine learning has worked in health to bring new technology into this sociotechnical environment, using data to support a vision of healthier living for everyone. Interdisciplinary fields of research like machine learning for health bring different values and judgements together, requiring that those value choices be deliberate and measured. More than just abstract ideas, our values are the basis upon which we choose our research topics, set up research collaborations, execute our research methodologies, make assessments of scientific and technical correctness, proceed to product development, and finally operationalize deployments and describe policy. For machine learning to achieve its aims of supporting healthier living while minimizing harm, we believe that a deeper introspection of our field’s values and contentions is overdue. In this perspective, we highlight notable areas in need of attention within the field. We believe deliberate and informed introspection will lead our community to renewed opportunities for understanding disease, new partnerships with clinicians and patients, and allow us to better support people and communities to live healthier, dignified lives.

    • Marzyeh Ghassemi
    • Shakir Mohamed
    Comment Open Access
  • Continued COVID-19 surges have highlighted the need for widespread testing in addition to vaccination for disease containment. SARS-COV-2 RNA can be found in faecal matter, making human stool another potential source for COVID-19 diagnostics. In this commentary, we highlight potential strategies to use a smart toilet platform to passively monitor COVID-19 surges, enabling earlier detection of infected individuals and promoting public health.

    • T. Jessie Ge
    • Carmel T. Chan
    • Seung-min Park
    Comment Open Access
  • Increasing digitization across the healthcare continuum has revolutionized medical research, diagnostics, and therapeutics. This digitization has led to rapid advancements in the development and adoption of Digital Health Technologies (DHT) by the healthcare ecosystem. With the proliferation of DHTs, the term ‘digital biomarker’ has been increasingly used to describe a broad array of measurements. Our objectives are to align the meaning of ‘digital biomarker’ with established biomarker terminology and to highlight opportunities to enable consistency in evidence generation and evaluation, improving the assessment of scientific evidence for future digital biomarkers.

    • Srikanth Vasudevan
    • Anindita Saha
    • Bakul Patel
    Comment Open Access
  • The vital signs—temperature, heart rate, respiratory rate, and blood pressure—are indispensable in clinical decision-making. These metrics are widely used to identify physiologic decline and prompt investigation or intervention. Vital sign monitoring is particularly important in acute care settings, where patients are at higher risk and may require additional vigilance. Conventional contact-based devices, while widespread and generally reliable, can be inconvenient or disruptive to patients, families, and staff. Non-contact, video-based methods present a more flexible and information-dense alternative that may enable creative improvements to patient care. Still, these approaches are susceptible to several sources of bias and require rigorous clinical validation. A recent study by Jorge et al. demonstrates that video-based monitoring can reliably capture heart rate and respiratory rate and overcome many potential sources of bias in post-operative settings. This presents real-world evaluation of a practical, noninvasive, and continuous monitoring technology that had previously only been tested in controlled settings.

    • James A. Diao
    • Jayson S. Marwaha
    • Joseph C. Kvedar
    Editorial Open Access
  • Delivery of serious illness communication (SIC) is necessary to ensure that all seriously ill patients receive goal-concordant care. However, the current SIC delivery process contains barriers that prevent the delivery of timely and effective SIC. In this paper, we describe the current bottlenecks of the traditional SIC workflow and explore how a hybrid artificial intelligence-human workflow may improve the efficiency and effectiveness of SIC delivery in busy practice settings.

    • Isaac S. Chua
    • Christine S. Ritchie
    • David W. Bates
    Comment Open Access
  • In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. The time-consuming and costly process of collecting annotations presents a sizable bottleneck for AI research and development. HALS (Human-Augmenting Labeling System) is a collaborative human-AI labeling workflow that uses an iterative “review-and-revise” model to improve the efficiency of this critical process in computational pathology.

    • James A. Diao
    • Richard J. Chen
    • Joseph C. Kvedar
    Editorial Open Access
  • Current public health measures catalyzed a large shift to virtual care, resulting in a great uptake in telephone and video-enabled care. While pre-pandemic public healthcare funding rarely covered the telephone as a reimbursable care delivery model, it has proven a crucial offering for many populations. As the new standard of virtual service delivery is being solidified, simple technological solutions that provide access to care must continue to be supported. This paper explores an important consequence of relying on complex technologies as the new standard of virtual care: the risk of exacerbating health disparities by enabling a deeper digital divide for marginalized populations.

    • Tyla Thomas-Jacques
    • Trevor Jamieson
    • James Shaw
    Comment Open Access
  • For the past century, health care measurement and delivery have been centered in hospitals and clinics. That is beginning to change as health measures and increasingly care delivery are migrating to homes and mobile devices. The COVID-19 pandemic has only accelerated this transition. While increasing access to care and improving convenience, this move toward platforms operated by for-profit firms raises concerns about privacy, equity, and duty that will have to be addressed. In addition, this change in measuring health and delivering health care will create opportunities for educators to expand the settings for training, researchers to conduct studies at enormous scale, payors to embrace lower-cost clinical settings, and patients to make their voices heard.

    • E. Ray Dorsey
    Comment Open Access