News & Comment

Digital health technologies (DHTs) can transform neurological assessments, improving quality and continuity of care. In the United States, the Food & Drug Administration (FDA) oversees the safety and efficacy of these technologies, employing a detailed regulatory process that classifies devices based on risk and requires rigorous review and post-market surveillance. Following FDA approval, DHTs enter the Current Procedural Terminology, Relative Value Scale Update Committee, and Centers for Medicare & Medicaid Services coding and valuation processes leading to coverage and payment decisions. DHT adoption is challenged by rapid technologic advancements, an inconsistent evidence base, marketing discrepancies, ambiguous coding guidance, and variable health insurance coverage. Regulators, policymakers, and payers will need to develop better methods to evaluate these promising technologies and guide their deployment. This includes striking a balance between patient safety and clinical effectiveness versus promotion of innovation, especially as DHTs increasingly incorporate artificial intelligence. Data validity, cybersecurity, risk management, societal, and ethical responsibilities should be addressed. Regulatory advances can support adoption of these promising tools by ensuring DHTs are safe, effective, accessible, and equitable.

In recent years the intersection of wearable technologies and machine learning (ML) based deep learning (DL) approaches have highlighted their potential in sleep research. Yet, a recent study published in NPJ Digital Medicine highlights the generalization limitations of DL models in sleep-wake classification using actigraphy data. Here, this article discusses some of the challenges and opportunities presented by domain adaptation and self-supervised learning (SSL), innovative methodologies that use large-scale unlabeled data to bolster the generalizability of DL models in sleep assessment. These approaches not only improve sleep-wake classification but also hold promise for extending to more comprehensive sleep stage classification, potentially advancing the field of automated sleep assessment through efficient and user-friendly wearable monitoring systems.

Recent developments in large language models (LLMs) have unlocked opportunities for healthcare, from information synthesis to clinical decision support. These LLMs are not just capable of modeling language, but can also act as intelligent “agents” that interact with stakeholders in open-ended conversations and even influence clinical decision-making. Rather than relying on benchmarks that measure a model’s ability to process clinical data or answer standardized test questions, LLM agents can be modeled in high-fidelity simulations of clinical settings and should be assessed for their impact on clinical workflows. These evaluation frameworks, which we refer to as “Artificial Intelligence Structured Clinical Examinations” (“AI-SCE”), can draw from comparable technologies where machines operate with varying degrees of self-governance, such as self-driving cars, in dynamic environments with multiple stakeholders. Developing these robust, real-world clinical evaluations will be crucial towards deploying LLM agents in medical settings.

Generative AI is designed to create new content from trained parameters. Learning from large amounts of data, many of these models aim to simulate human conversation. Generative AI is being applied to many different sectors. Within healthcare there has been innovation specifically towards generative AI models trained on electronic medical record data. A recent review characterizes these models, their strengths, and weaknesses. Inspired by that work, we present our evaluation checklist for generative AI models applied to electronic medical records.

In the medical literature, promising results regarding accuracy of medical AI are presented as claims for its potential to increase efficiency. This elision of concepts is misleading and incorrect. First, the promise that AI will reduce human workload rests on a too narrow assessment of what constitutes workload in the first place. Human operators need new skills and deal with new responsibilities, these systems need an elaborate infrastructure and support system that all contribute to an increased amount of human work and short-term efficiency wins may become sources of long-term inefficiency. Second, for the realization of increased efficiency, the human-side of technology implementation is determinate. Human knowledge, competencies and trust can foster or undermine efficiency. We conclude that is important to remain conscious and critical about how we talk about expected benefits of AI, especially when referring to systemic changes based on single studies.

Boussina et al. recently evaluated a deep learning sepsis prediction model (COMPOSER) in a prospective before-and-after quasi-experimental study within two emergency departments at UC San Diego Health, tracking outcomes before and after deployment. Over the five-month implementation period, they reported a 17% relative reduction in in-hospital sepsis mortality and a 10% relative increase in sepsis bundle compliance. This editorial discusses the importance of shifting the focus towards evaluating clinically relevant outcomes, such as mortality reduction or quality-of-life improvements, when adopting artificial intelligence (AI) tools. We also explore the ecosystem vital for AI algorithms to succeed in the clinical setting, from interoperability standards and infrastructure to dashboards and action plans. Finally, we suggest that algorithms may eventually fail due to the human nature of healthcare, advocating for the need for continuous monitoring systems to ensure the adaptability of these tools in the ever-evolving healthcare landscape.

The utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used unimodal AI models, multimodal approaches were superior because they integrate multiple types of data. However, creating multimodal models and determining model performance are challenging tasks given the multi-factored nature of diabetes. For both unimodal and multimodal models, there are also concerns of bias with the lack of external validations and representation of race, age, and gender in training data. The barriers in data quality and evaluation standardization are ripe areas for developing new technologies, especially for entrepreneurs and innovators. Collaboration amongst providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care is providing quality and equitable patient care.

We explore the evolving landscape of diagnostic artificial intelligence (AI) in dermatology, particularly focusing on deep learning models for a wide array of skin diseases beyond skin cancer. We critically analyze the current state of AI in dermatology, its potential in enhancing diagnostic accuracy, and the challenges it faces in terms of bias, applicability, and therapeutic recommendations.

Historically, the Centers for Medicare and Medicaid Services (CMS) has formed partnerships with select private sector entities, including large traditional hospital and health system networks, nursing homes, and payer groups. However, innovations from technology-enabled services companies and digital technology companies are uniquely poised to aid CMS in addressing key barriers toward advancing its mission of improving healthcare access and equity. There are four pivotal opportunity areas where partnerships with technology businesses and tools would enhance the work of CMS: (1) improving consumer awareness about CMS programs, (2) mitigating access gaps through virtual care programs, (3) streamlining the complexity of different payer plan models, and (4) using technology-enabled services to address social risk factors without imposing additional burdens on providers. We offer examples of digital and technology-enabled solutions that improve patient access to care and close equity gaps, as well as propose specific recommendations for CMS to advance and expand the reach and impact of these solutions. Namely, these recommendations include partnerships with private sector companies that can educate and support consumers about their benefits, the extension of telehealth reimbursement parity for virtual care solutions, allowing for cross-state licensure across plans and reimbursement for care coordination services that alleviate provider burden to screen and address patients’ social determinants of health needs. We argue that CMS has an imperative role in leveraging the innovations of technology-enabled services and digital health technologies to lower healthcare access barriers, mitigate provider burden, stimulate innovation, and close equity gaps at the patient, provider, and innovator levels.

Navigating contemporary healthcare, wearable technology and smartphones are marking the dawn of a transformative era in patient observation and personalised care. Wearables, equipped with various sensing technologies (e.g., accelerometer for movement, optics for heart rate), are increasingly being recognised for their expansive potential in (remote) patient monitoring, diagnostics, and therapeutic applications which suggests a plausible move towards a more decentralised healthcare system. This shift is evident as healthcare providers and patients alike are becoming increasingly accepting of wearable-driven tools, as they enable continuous health monitoring outside of traditional clinical settings. Equally, the ubiquitous nature of smartphones, now more than mere communication tools, is being harnessed to serve as pivotal health monitoring instruments. Their added sensing capabilities with Internet of Things (IoT) driven connectivity enable a (relatively) seamless transition from conventional health practices to a more interconnected, digital age. However, this evolving landscape is not without its challenges, with concerns surrounding data privacy, security, and ensuring equitable access to digital advances. As we delve deeper into digital healthcare, we must harness the full potential of those technologies and ensure their ethical and equitable implementation, envisioning a future where healthcare is not just hospital-centric but is part of our daily lives.

The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies within healthcare settings holds the potential to close the AI translation gap by establishing a robust framework that accelerates the safe, ethical, and effective delivery of AI/ML in day-to-day patient care. Healthcare organizations (HCOs) can implement these principles effectively by embracing an enterprise QMS analogous to those in regulated industries. By establishing a QMS explicitly tailored to health AI technologies, HCOs can comply with evolving regulations and minimize redundancy and rework while aligning their internal governance practices with their steadfast commitment to scientific rigor and medical excellence.

As result of the pandemic-related increase in telehealth and the 21st Century Cures Act, technology is playing an increasing role in healthcare. This has led to organizational investments in the “digital front door” of healthcare. The promise that these technologies can revolutionize care by better connecting us to our patients, overcoming analog barriers to care, and addressing health disparities is grounded in “techno-optimism.” We advocate for organizational leaders to inform their digital health equity strategies with a balanced measure of “techno-skepticism”, grounded in implementation science, that can ensure alignment between health technology and health equity.

At a time of diminishing clinical skills and diagnostic endeavours for clinicians worldwide, a method of rapid automated microscopy coupled with pH measurement is introduced with early clinical success results in women with common vulvovaginal symptoms facilitating rapid diagnosis and enhanced therapeutic measures.

Sleep recordings are visually classified in stages by experts in the field, based on consensus international criteria. This procedure is expensive and time-consuming. Automatic sleep scoring systems have, progressively over the years, demonstrated good levels of accuracy. Although the performance of these algorithms is believed to be high, however, there remains widespread skepticism in their daily use in clinical and scientific practice. In this comment to a recent publication of NPJ Digital Medicine, we express the reasons why we think the sleep expert should remain the central pivot in the pendulum between visual and automatic methodology, trying to find a new balance in the scientific debate.

Contemporary wearables like smartwatches are often equipped with advanced sensors and have associated algorithms to aid researchers monitor physiological outcomes like physical activity levels, sleep patterns or heart rate in free-living environments. But here’s the catch: all that valuable data is often collected separately because the sensors don’t always play nice with each other, and it’s a real challenge to put all the data together. To get the full picture, we may often need to combine different data streams. It’s like putting together a puzzle of our health, instead of just looking at individual pieces. This way, we can gather more useful info and better understand health (it’s called digital twinning). Yet, to do so requires robust sensor/data fusion methods at the signal, feature, and decision levels. Selecting the appropriate techniques based on the desired outcome is crucial for successful implementation. An effective data fusion framework along with the right sensor selection could contribute to a more holistic approach to health monitoring that extends beyond clinical settings.

Gregoor et al. evaluated the healthcare implications and costs of an AI-enabled mobile health app for skin cancer detection, involving 18,960 beneficiaries of a Netherlands insurer. They report a 32% increase in claims for premalignant and malignant skin lesions among app users, largely attributed to benign skin lesions and leading to higher annual costs for app users (€64.97) compared to controls (€43.09). Cost-effectiveness analysis showed a comparable cost to dermatologist-based diagnosis alone. This editorial emphasizes the balance in AI-based dermatology between increased access and increased false positives resulting in overutilization. We suggest refining the diagnostic schemas with new referral pathways to capitalize on potential savings. We also discuss the importance of econometric analysis to evaluate the adoption of new technologies, as well as adapting payment models to mitigate the risk of overutilization inherent in AI-based diagnostics such as skin cancer detection.