Journal of Medical Internet Research

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates.

The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the review process for such manuscripts often go through multiple review cycles to enhance the quality and completeness of reporting. The use of reporting guidelines or checklists can help ensure consistency in the quality of submitted (and published) scientific manuscripts and, for example, avoid instances of missing information. In this Editorial, the editors of JMIR Publications journals discuss the general JMIR Publications policy regarding authors’ application of reporting guidelines and specifically focus on the reporting of ML studies in JMIR Publications journals, using the Consolidated Reporting of Machine Learning Studies (CREMLS) guidelines, with an example of how authors and other journals could use the CREMLS checklist to ensure transparency and rigor in reporting.

Clinical notes contain contextualized information beyond structured data related to patients’ past and current health status.

This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT’s performance with human annotators’ in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approximated human results, with a high degree of agreement noted: 94.4% (9436/10,000) for any AE detection (Fleiss κ=0.95) and 99.3% (9931/10,000) for serious AEs (κ=0.96). These findings suggest that ChatGPT has the potential to replicate human annotation accurately and efficiently. The study recognizes possible limitations, including concerns about the generalizability due to ChatGPT’s training data, and prompts further research with different models, data sources, and content analysis tasks. The study highlights the promise of large language models for enhancing the efficiency of biomedical research.

To overcome knowledge gaps and optimize long-term follow-up (LTFU) care for childhood cancer survivors, the concept of the Survivorship Passport (SurPass) has been invented. Within the European PanCareSurPass project, the semiautomated and interoperable SurPass (version 2.0) will be optimized, implemented, and evaluated at 6 LTFU care centers representing 6 European countries and 3 distinct health system scenarios: (1) national electronic health information systems (EHISs) in Austria and Lithuania, (2) regional or local EHISs in Italy and Spain, and (3) cancer registries or hospital-based EHISs in Belgium and Germany.

Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Valid assessment tools are needed when investigating adherence to national dietary and lifestyle guidelines.

Digital health interventions (DHIs) have shown promising results in enhancing the management of heart failure (HF). Although health care interventions are increasingly being delivered digitally, with growing evidence on the potential cost-effectiveness of adopting them, there has been little effort to collate and synthesize the findings.

Neglected tropical diseases (NTDs) affect over 1.5 billion people worldwide, primarily impoverished populations in low- and middle-income countries. Skin NTDs, a significant subgroup, manifest primarily as skin lesions and require extensive diagnosis and treatment resources, including trained personnel and financial backing. The World Health Organization has introduced the SkinNTDs app, a mobile health tool designed to train and be used as a decision support tool for frontline health care workers. As most digital health guidelines prioritize the thorough evaluation of mobile health interventions, it is essential to conduct a rigorous and validated assessment of this app.

There is a dearth of feasibility assessments regarding using large language models (LLMs) for responding to inquiries from autistic patients within a Chinese-language context. Despite Chinese being one of the most widely spoken languages globally, the predominant research focus on applying these models in the medical field has been on English-speaking populations.

Pain is a biopsychosocial phenomenon, resulting from the interplay between physiological and psychological processes and social factors. Given that humans constantly interact with others, the effect of social factors is particularly relevant. Documenting the significance of the social modulation of pain, an increasing number of studies have investigated the effect of social contact on subjective pain intensity and pain-related physiological changes. While evidence suggests that social contact can alleviate pain, contradictory findings indicate an increase in pain intensity and a deterioration of pain coping strategies. This evidence primarily stems from studies examining the effect of social contact on pain within highly controlled laboratory conditions. Moreover, pain assessments often rely on one-time subjective reports of average pain intensity across a predefined period. Ecological momentary assessments (EMAs) can circumvent these problems, as they can capture diverse aspects of behavior and experiences multiple times a day, in real time, with high resolution, and within naturalistic and ecologically valid settings. These multiple measures allow for the examination of fluctuations of pain symptoms throughout the day in relation to affective, cognitive, behavioral, and social factors. In this opinion paper, we review the current state and future relevance of EMA-based social pain research in daily life. Specifically, we examine whether everyday-life social support reduces or enhances pain. The first part of the paper provides a comprehensive overview of the use of EMA in pain research and summarizes the main findings. The review of the relatively limited number of existing EMA studies shows that the association between pain and social contact in everyday life depends on numerous factors, including pain syndromes, temporal dynamics, the nature of social interactions, and characteristics of the interaction partners. In line with laboratory research, there is evidence that everyday-life social contact can alleviate, but also intensify pain, depending on the type of social support. Everyday-life emotional support seems to reduce pain, while extensive solicitous support was found to have opposite effects. Moreover, positive short-term effects of social support can be overshadowed by other symptoms such as fatigue. Overall, gathering and integrating experiences from a patient’s social environment can offer valuable insights. These insights can help interpret dynamics in pain intensity and accompanying symptoms such as depression or fatigue. We conclude that factors determining the reducing versus enhancing effects of social contact on pain need to be investigated more thoroughly. We advocate EMA as the assessment method of the future and highlight open questions that should be addressed in future EMA studies on pain and the potential of ecological momentary interventions for pain treatment.