Tiffany I. Leung

To align with contemporary trends and place greater focus on persistent and novel challenges in the field, JMIR Medical Informatics has updated its focus and scope. Each submitted work will be evaluated against a stronger criterion for translational impact. Additionally, submissions on AI methods and applications are expected to meet standards of rigor and reporting transparency. In this editorial, we explain further what changed; why; and what this means for prospective authors, reviewers, and readers.

The value of a meta-analysis is based on its methodological and statistical rigor, yet many published systematic reviews and meta-analyses contain statistical shortcomings that limit their utility for clinical practice and public health. This can make it challenging to aggregate data for treatment choices for patients as well as limit the extent to which policymakers can promote social change and improve public health. This challenge is compounded by the traditionally slow and resource-intensive nature of systematic reviews, which delays the translation of vital evidence. In this tutorial, we address both challenges. We first provide a primer on essential statistical techniques to help authors produce more robust and reliable meta-analyses. We then briefly discuss the growing role of artificial intelligence (AI) in automating tasks in systematic literature reviews and meta-analyses. Ethical use and disclosure of AI in supporting these essential tasks are also important considerations. This guide is intended to help authors enhance the rigor of their work and use new technologies to ensure their findings are both trustworthy and timely.

Clinical documentation is a foundational skill in medicine, developed during training and required in everyday practice. Historically, the chart note functioned as a clinician-centered cognitive tool for reasoning, teaching, and communication but has evolved into a multipurpose document shaped by administrative, regulatory, and financial demands, and is increasingly experienced as burdensome. The electronic health record, intended to improve efficiency, has introduced additional complexity and workflow strain, contributing to clinician burnout. Ambient artificial intelligence (AI) scribe technologies are rapidly being adopted to address these challenges, yet their implementation has outpaced evidence regarding their impact on learning, cognition, and clinical reasoning. We raise questions regarding the underexplored consequences of AI-assisted documentation, particularly cognitive off-loading and the potential for de-skilling, echoing historical concerns surrounding earlier cognitive technologies that externalized thought. We propose a practical framework that re-centers clinical documentation around four core aims: supporting clinical reasoning (“note to self”), facilitating communication (“note to others”), meeting medicolegal and billing requirements, and enhancing patient education in the era of open notes. Incorporating this framework into training may promote more intentional documentation practices before routine reliance on AI. We advocate for reframing the chart note to support clinician development and preserve its role in high-quality, patient-centered care.

UNSTRUCTURED Widespread implementation of ambient artificial intelligence (AI) scribe technologies in the United States and globally continues to outpace available evidence on learning and cognition in medical education and practice. We explore the often-ignored consequences of ambient AI scribe use within clinical practice and medical training, such as cognitive offloading. We also introduce the concept of four fundamental purposes of the clinical chart note based on the long history of medical documentation as a framework for clinical teaching, mitigation of administrative burdens and evaluation of AI Scribe development and use.

UNSTRUCTURED The value of a meta-analysis is based on its methodological and statistical rigor, yet many published systematic reviews and meta-analyses contain statistical shortcomings that limit their utility for clinical practice and public health. This can make it challenging to aggregate data for treatment choices for patients as well as limit the extent to which policymakers can promote social change and improve public health. This challenge is compounded by the traditionally slow and resource-intensive nature of systematic reviews, which delays the translation of vital evidence. In this tutorial, we address both challenges. We first provide a primer on essential statistical techniques to help authors produce more robust and reliable meta-analyses. We then briefly discuss the growing role of artificial intelligence (AI) in automating tasks in systematic literature reviews and meta-analyses. Ethical use and disclosure of AI in supporting these essential tasks are also important considerations. This guide is intended to help authors enhance the rigor of their work and use new technologies to ensure their findings are both trustworthy and timely.

Background: Internists formulate diagnostic hypotheses and personalized treatment plans by integrating data from a comprehensive clinical interview, reviewing a patient’s medical history, physical examination and findings from complementary tests. The patient treatment life cycle generates a significant volume of data points that can offer valuable insights to improve patient care by guiding clinical decision-making. Artificial Intelligence (AI) and, in particular, Generative AI (GAI), are promising tools in this regard, particularly after the introduction of Large Language Models. The European Federation of Internal Medicine (EFIM) recognizes the transformative impact of AI in leveraging clinical data and advancing the field of internal medicine. This position paper from the EFIM explores how AI can be applied to achieve the goals of P6 Medicine principles in internal medicine. P6 Medicine is an advanced healthcare model that extends the concept of Personalized Medicine toward a holistic, predictive, patient-centered approach that also integrates psycho-cognitive and socially responsible dimensions. An additional concept introduced is that of Digital Therapies (DTx), software applications designed to prevent and manage diseases and disorders through AI, which are used in the clinical setting if validated by rigorous research studies. Methods: The literature examining the relationship between AI and Internal Medicine was investigated through a bibliometric analysis. The themes identified in the literature review were further examined through the Delphi method. Thirty international AI and Internal Medicine experts constituted the Delphi panel. Results: Delphi results were summarized in a SWOT Analysis. The evidence is that through extensive data analysis, diagnostic capacity, drug development and patient tracking are increased. Conclusions: The panel unanimously considered AI in Internal Medicine as an opportunity, achieving a complete consensus on the matter. AI-driven solutions, including clinical applications of GAI and DTx, hold the potential to strongly change internal medicine by streamlining workflows, enhancing patient care and generating valuable data.

The administrative burden of clinical documentation contributes to health care practitioner burnout and diverts valuable time away from direct patient care. Ambient artificial intelligence (AI) scribes—also called “digital scribes” or “AI scribes”—are emerging as a promising solution, given their potential to automate clinical note generation and reduce clinician workload, and those specifically built on a large language model (LLM) are emerging as technologies for facilitating real-time clinical documentation tasks. This potentially transformative development has a foundation on longer-standing, AI-based transcription software, which uses automated speech recognition and/or natural language processing. Recent studies have highlighted the potential impact of ambient AI scribes on clinician well-being, workflow efficiency, documentation quality, user experience, and patient interaction. So far, limited evidence indicates that ambient AI scribes are associated with reduced clinician burnout, lower cognitive task load, and significant time savings in documentation, particularly in after-hours electronic health record (EHR) work. One consistently reported benefit is the improvement in the patient-physician interaction, as physicians feel more present during a clinical encounter. However, these benefits are counterbalanced by persisting concerns regarding the accuracy, consistency, language use, and style of AI-generated notes. Studies noting errors, omissions, or hallucinations caution that diligent clinician oversight is necessary. The user experience is also heterogeneous, with benefits varying by specialty and individual workflow. Further, there are concerns about ethical and legal issues, algorithmic bias, the potential for long-term “cognitive debt” from overreliance on AI, and even the potential loss of physician autonomy. Additional pragmatic concerns include security, privacy, integration, interoperability, user acceptance and training, and the cost-effectiveness of adoption at scale. Finally, limited studies describe adoption or evaluation of these technologies by nonphysician clinicians and health professionals. Although ambient AI scribes and AI-driven documentation technologies are promising as potentially practice-changing tools, there are many questions remaining. Key issues persist, including responsible deployment with the goal of ensuring that ambient AI scribes produce clinical documentation that supports more efficient, equitable, and patient-centered care. To advance our collective understanding and address key issues, JMIR Medical Informatics is launching a call for papers for a new section on “Ambient AI Scribes and AI-Driven Documentation Technologies.” As editors, we look forward to the opportunity to advance the science and understanding of these fields through publishing high-quality and rigorous scholarly work in this new section of JMIR Medical Informatics.

Primary care informatics (PCI) professionals address workflow and technology solutions in a wide spectrum of health, ranging from optimizing the experience of the individual patient in the clinic room to supporting the health of populations and augmenting the work of frontline primary care clinical teams. PCI overlaps uniquely with 2 disciplines with an impact on societal health—primary care and health informatics. Primary care is a gateway to health care access and aims to synthesize and coordinate numerous, complex elements of patients’ health and medical care in a holistic manner. However, over the past 25 years, primary care has become a specialty in crisis: in a post–COVID-19 world, workforce shortages, clinician burnout, and continuing challenges in health care access all contribute to difficulties in sustaining primary care. Informatics professionals are poised to change this trajectory. In this viewpoint, we aim to inform readers of the discipline of PCI and its importance in the design, support, and maintenance of essential primary care services. Although this work focuses on primary care in the United States, which includes general internal medicine, family medicine, and pediatrics (and depending on definition, includes specialties such as obstetrics and gynecology), many of the principles outlined can also be applied to comparable health care services and settings in other countries. We highlight (1) common global challenges in primary care, (2) recent trends in the evolution of PCI (personalized medicine, population health, social drivers of health, and team-based care), and (3) opportunities to move forward PCI with current and emerging technologies using the 4Cs of primary care framework. In summary, PCI offers important contributions to health care and the informatics field, and there are many opportunities for informatics professionals to enhance the primary care experience for patients, families, and their care teams.

Previous work has suggested that gender and geographical distribution (affiliation) of Editors-in-Chief (EiC) and Editorial Board (EB) members are inequitable in representation of scientific communities, and could benefit from increasing diversity of representation. Specifically, previous studies suggest that male and ethnically white (or non-minoritized groups) are overrepresented. Such differences in representation may potentially influence the scientific and scholarly record. This paper aims to build on pre-existing literature by examining the diversity of representation among EiCs and EB members in the top (Q1) journals in the “Medicine-Health Informatics” category (ranked by SCImago Journal and Country Rank, or SJR) in terms of gender as assessed by genderize.io) and geographical distribution of affiliations. Preliminary findings are consistent with those of previous work on the topic: only 25% (8/32) of the EiCs in the selected journals are female, while females only represent 32.7% (426/1303) of the EB members across journals. Furthermore, the US is highly represented in EBs, with more than half of the members, i.e., 52.2% (698/1337), being US-affiliated. Present results suggest the need for an intentional approach to diversifying representation on editorial boards of medical informatics journals. Such intention can be seen as part of a call to action from important stakeholders, including medical informatics leaders and programs, journal management and publishers, and the medical informatics and scientific community more generally.