News & Comment

Machine learning models in healthcare aim to predict critical outcomes but often overlook existing Early Warning Systems’ impact. Using data from King’s College Hospital, we demonstrate how current evaluation methods can lead to paradoxical results. We discuss challenges in developing ML models from retrospective data and propose a novel approach focused on identifying when patients enter a ‘risk state’ through latent health representations, potentially transforming clinical decision-making.

Liu et al.’s recent study reveals that telemedicine expanded access to cardiovascular care in China, enabling patients in poorer areas of the country to access care in cities with more resources. While these findings may support the global expansion of telemedicine, implementation often proves challenging. This article examines the potential and limitations of adopting similar telemedicine efforts within the U.S. to advance geographic health equity.

Artificial intelligence (AI) is increasingly permeating the fabric of medicine, but getting full benefits will likely require fundamental changes in practice. Accepting this will be challenging for many clinicians. However, it may be necessary to ensure that AI’s ambitious promises translate into real-life improvement.

Electronic health records (EHRs) secondary usage with large language models (LLMs) raise privacy challenges. National regulations like GDPR and HIPAA offer protection frameworks, but specific strategies are needed to mitigate risk in generative AI. Risks can be reduced by using strategies like privacy-preserving locally deployed LLMs, synthetic data generation, differential privacy, and deidentification. Depending on the task, strategies should be employed to increase compliance with patient privacy regulatory frameworks.

Can artificial intelligence improve clinical trial design? Despite their importance in medicine, over 40% of trials involve flawed protocols. We introduce and propose the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by regulatory agencies, customization by Health Technology Assessment bodies, and deployment to stakeholders. This strategy could enhance trial efficiency, inclusivity, and safety, leading to more representative, cost-effective clinical trials.

Alzheimer’s disease is the fifth-leading cause of death for adults over the age of 65. Retinal imaging has emerged to find more accurate diagnostic tool for Alzheimer’s Disease. This paper highlights Hao et al.’s development of a new deep learning tool, EyeAD, which studies Optical Coherence Tomography Angiography (OCT-A) of patients with Alzheimer’s. Integrating this model into clinical workflows may offer novel insights into the progression of this disease.

Qiao et al. recently investigated the ability of dual-energy X-ray absorptiometry (DXA) scans and a smartphone app to provide detailed body composition and shape data. In a healthcare system that continues to rely on crude and stigmatizing measurements like body-mass index (BMI), their findings point to the potential of newer technologies to capture markers (i.e., visceral adiposity and fat distribution patterns) that provide clearer insights into metabolic health.

Current regulatory frameworks for artificial intelligence-based clinical decision support (AICDS) are insufficient to ensure safety, effectiveness, and equity at the bedside. The oversight of clinical laboratory testing, which requires federal- and hospital-level involvement, offers many instructive lessons for how to balance safety and innovation and warnings regarding the fragility of this balance. We propose an AICDS oversight framework, modeled after clinical laboratory regulation, that is deliberative, inclusive, and collaborative.

The introduction of the electronic health record was heralded as a technology solution to improve care quality and efficiency, but these tools have contributed to increased administrative burden and burnout for clinicians. Today, artificial intelligence is receiving much of the same attention and promises as electronic health records. Can healthcare learn from the failures of electronic health records to maximize the potential of artificial intelligence?

Radin et al.’s recent study on patients with long COVID demonstrates that personal wearable data can provide critical insight into complex conditions. This editorial argues that research insights gained through personal wearables support the integration of personal wearables into healthcare. Challenges in incorporating wearable data in the clinic point towards AI data sorting, data sharing, device interoperability, FDA oversight, and expanded insurance coverage as first steps towards addressing these challenges.

India’s evolving digital health strategy leverages innovative technologies to enhance access to healthcare services. This paper explores the key components of India’s digital health transformation, including the Ayushman Bharat Digital Mission (ABDM) and India’s integration of biometric identification and digital infrastructure to improve healthcare delivery. The lessons learned from India’s large-scale implementation of digital health provide valuable insights for global health markets and digital transformations in healthcare systems.

Claims of AI outperforming medical practitioners are under scrutiny, as the evidence supporting many of these claims is not convincing or transparently reported. These claims often lack specificity, contextualization, and empirical grounding. In this comment, we offer constructive ethical guidance that can benefit authors, journal editors, and peer reviewers when reporting and evaluating findings in studies comparing AI to physician performance. The guidance provided here forms an essential addition to current reporting guidelines for healthcare studies using machine learning.

This piece critiques the exclusion of healthcare practitioners (HCPs) from the digital health innovation process. Drawing on “Sync fast and solve things—best practices for responsible digital health” by Landers et al., the editorial argues for the importance of inclusive co-creation, in which clinicians play an active role in developing digital health solutions. It emphasizes that without the meaningful involvement of HCPs, digital health tools risk being clinically irrelevant.

The Remote Assessment of Disease and Relapse – Alzheimer’s Disease (RADAR-AD) consortium evaluated remote measurement technologies (RMTs) for assessing functional status in AD. The consortium engaged with the European Medicines Agency (EMA) to obtain feedback on identification of meaningful functional domains, selection of RMTs and clinical study design to assess the feasibility of using RMTs in AD clinical studies. We summarized the feedback and the lessons learned to guide future projects.

There is a need for digital health innovation focused on bettering the health of marginalized populations. These communities, often insured by Medicaid and Medicare, face complex healthcare barriers that technology can address—emphasizing the role of the Center for Medicaid and Medicare Services (CMS) in fostering innovation. Dasari et al. identify four areas of CMS collaboration with startups: enhancing consumer awareness, leveraging telehealth, streamlining cross-state licensing and billing, and adopting technology-enabled tools.

Decentralized clinical trials have gained in popularity over the last years due to their advantages related to broadening recruitment strategies and resource saving possibilities. As more clinical trials adopt decentralized strategies, it is essential to share the knowledge about both successful and unsuccessful efforts in the research community. In the present commentary, we explore potential reasons that led to early termination of a decentralized clinical trial in Oncology.

Digital health technologies (DHT) offer the ability to deliver personalized care, lower barriers to access, and positively impact health outcomes. However, DHT utilization is impacted by insufficient market access pathways. A policy “full-stack”—including regulatory authorization, product value assessment, pricing and reimbursement, and patient access infrastructure—offers a framework for DHT integration into national healthcare ecosystems. Consistent clinical evidence requirements across national jurisdictions will further increase DHT scalability.

The European Union’s recently adopted Artificial Intelligence (AI) Act is the first comprehensive legal framework specifically on AI. This is particularly important for the healthcare domain, as other existing harmonisation legislation, such as the Medical Device Regulation, do not explicitly cover medical AI applications. Given the far-reaching impact of this regulation on the medical AI sector, this commentary provides an overview of the key elements of the AI Act, with easy-to-follow references to the relevant chapters.

In the face of formidable healthcare challenges, such as staffing shortages and rising costs, technology has emerged as a crucial ally in enhancing patient care. UCHealth, Colorado’s largest health system, has pioneered the integration of technology into patient care through its Virtual Health Center (VHC). In this Comment, we explore UCHealth’s journey in creating a centralized hub that harnesses innovative digital health solutions to address patient care needs across its 12 hospitals, spanning over 600,000 emergency department visits and nearly 150,000 inpatient and observation encounters annually. The VHC has proven to be a transformative force, providing high-quality care at scale, reducing staff burden, and establishing new career pathways in virtual health. The transformation process involved multiple steps: (a) identifying a need, (b) vetting within health system solutions, (c) searching for industry solutions, and scrutinizing these through meetings with our innovations center, (d) piloting the solution, and (e) sustaining the solution by integrating them within the electronic health record (EHR).

We have entered a new age of health informatics—applied health informatics—where digital health innovation cannot be pursued without considering operational needs. In this new digital health era, creating an integrated applied health informatics system will be essential for health systems to achieve informatics healthcare goals. Integration of information technology (IT) and health informatics does not naturally occur without a deliberate and intentional shift towards unification. Recognizing this, NYU Langone Health’s (NYULH) Medical Center IT (MCIT) has taken proactive measures to vertically integrate academic informatics and operational IT through the establishment of the MCIT Department of Health Informatics (DHI). The creation of the NYULH DHI showcases the drivers, challenges, and ultimate successes of our enterprise effort to align academic health informatics with IT; providing a model for the creation of the applied health informatics programs required for academic health systems to thrive in the increasingly digitized healthcare landscape.