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Received — 16 September 2025 ⏭ Favourites

A Practical Guide to Using Futures Methods in Health Care: Approaches, Applications, and Case Studies

✇Journal of Medical Internet Research
By: Bertalan Mesko · Tamás Kristóf · Pranavsingh Dhunnoo · Nóra Árvai · Gellért Katonai
16 September 2025 at 21:00
Researchers and health care institutions have increasingly applied structured futures methods—such as the futures wheel, scenario analysis, forecasting, and horizon scanning—to systematically explore, generate, and prepare for multiple possible futures. However, discussions around the future of medicine, specialties, or therapeutic areas have often relied on the subjective opinions or perspectives of key opinion leaders rather than on future strategies, policies, visions, and scenarios that are grounded in rigorous and established methods. This underscores the need for futures methods to be widely adopted and effectively incorporated into both medical practice and health care policymaking. Integrating structured foresight techniques into strategic planning enables clinicians and policymakers to transition from reactive decision-making to proactive, plausible approaches that shape a more resilient and adaptive health care system. Our goal with this paper is to provide a methodological guide that is supported by case studies, demonstrating how futures methods can be systematically applied in health care. By offering practical examples, we intend to empower medical professionals, health care leaders, researchers, patients, and policymakers with the tools to anticipate and navigate future challenges and opportunities more effectively.

Digital Health Technology Infrastructure Challenges to Support Health Equity in the United States: Scoping Review

✇Journal of Medical Internet Research
By: Shongkour Roy · Stella T Lartey · Polina Durneva · Niharika Jha · Michael Arthur Ofori · Zebunnesa Zeba · Stella Dockery · Nichole Saulsberry Scarboro · Michelle Taylor · Ashish Joshi
16 September 2025 at 05:15
Background: Even though Digital Health Technology (DHT) is widely utilized in the United States (U.S.) at both hospital provider and individual levels, it is beset with several challenges that have contributed to inequities in the health service delivery. Previous studies have shown that health inequities observed may be amplified many by DHT requirements. Objective: The objectives of this scoping review are aimed at synthesizing information on DHT inequities by exploring evidence that describes DHT infrastructure needs focused on promoting health equity in the U.S. and identifying key challenges at both the individual/patient level and at the health service provider's level. Methods: We adapted Arksey and O'Malley's scoping review guidelines in our review. We searched PubMed, Web of Science, CINAHL, and PsycINFO were searched. We also conducted supplementary searches on Google Scholar. The inclusion criteria were peer-reviewed publications that broadly conceptualize or analyze DHT infrastructure from a health equity perspective and the challenges of DHT requirements between 2020 and 2024. Following a full-text screening using eligibility criteria such as studies were included if they examined DHT infrastructure in the U.S. from a health equity perspective, discussed health disparities resulting from DHT interventions, or investigated the variables influencing health inequities connected to DHT. Two researchers evaluated each citation’s individually at the title and abstract levels. Thematic approach and qualitative analysis determined this scoping review’s outcome. Results: Of the 628 research articles from the search, 27 were included in the analysis based on the inclusion criteria. In this review, we discussed factors such as elderly population, education, race, ethnicity, and socioeconomic status leading to health inequities in DHT. Patients and Service providers challenges that exist in health inequities related to DHT. The most common challenges for service providers were infrastructure and technical issues such as inadequate integration with existing workflows, user-unfriendly health information exchange (HIE) interfaces, and lack of skilled staff, while for individuals or patients, this included limited broadband internet access, cultural or linguistic appropriateness, and access to digital tools. Conclusions: The study identified that in the U.S., DHT is an essential part of the delivery of health services, yet it is saddled with key challenges leading to health inequities. Finding pragmatic solutions to these challenges can improve health equity in DHT.

Prompt Engineering in Clinical Practice: Tutorial for Clinicians

✇Journal of Medical Internet Research
By: Jialin Liu · Fang Liu · Changyu Wang · Siru Liu
16 September 2025 at 03:00
Large language models (LLMs), such as OpenAI’s GPT series and Google’s PaLM, are transforming healthcare by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, where small changes in wording or structure can greatly impact output quality. This poses a challenge for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot, few-shot, chain-of-thought, and meta-prompting. We examine four critical dimensions (accuracy, bias mitigation, privacy protection, and workflow integration) through clinical case studies grounded in real-world practice. We provide actionable guidance on defining objectives, applying core principles, iteratively refining prompts, and integrating them into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.
  • ✇InfoQ
  • Hugging Face Releases FinePDFs: a 3-Trillion-Token Dataset Built from PDFs Robert Krzaczyński
    Hugging Face has unveiled FinePDFs, the largest publicly available corpus built entirely from PDFs. The dataset spans 475 million documents in 1,733 languages, totaling roughly 3 trillion tokens. At 3.65 terabytes in size, FinePDFs introduces a new dimension to open training datasets by tapping into a resource long considered too complex and expensive to process. By Robert Krzaczyński
     
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Hugging Face Releases FinePDFs: a 3-Trillion-Token Dataset Built from PDFs

✇InfoQ
By: Robert Krzaczyński
15 September 2025 at 16:55

Hugging Face has unveiled FinePDFs, the largest publicly available corpus built entirely from PDFs. The dataset spans 475 million documents in 1,733 languages, totaling roughly 3 trillion tokens. At 3.65 terabytes in size, FinePDFs introduces a new dimension to open training datasets by tapping into a resource long considered too complex and expensive to process.

By Robert Krzaczyński

Do startups still need Silicon Valley? Leaders at SignalFire, Lago, and Revolution debate at TechCrunch Disrupt 2025

✇TechCrunch
By: TechCrunch Events
24 September 2025 at 01:15
Does Silicon Valley still give founders an edge? At TechCrunch Disrupt 2025, Anh-Tho Chuong (Lago), David Hall (Revolution), and Tawni Nazario-Cranz (SignalFire) debate whether location still drives startup success.

Primary care detection of Alzheimer’s disease using a self-administered digital cognitive test and blood biomarkers

✇Nature Medicine
By: Pontus Tideman · Linda Karlsson · Olof Strandberg · Susanna Calling · Ruben Smith · Patrik Midlöv · Philip B. Verghese · Joel B. Braunstein · Niklas Mattsson-Carlgren · Erik Stomrud · Sebastian Palmqvist · Oskar Hansson
15 September 2025 at 08:00

Nature Medicine, Published online: 15 September 2025; doi:10.1038/s41591-025-03965-4

A brief, self-administered digital cognitive test, in combination with a blood test, accurately detects clinical Alzheimer’s disease in primary care.
Received — 15 September 2025 ⏭ Favourites

Prognostic Value of Circulating Tumor DNA in HR+/HER2- Stage I-III Breast Cancer: A Systematic Review

✇MRD
By: Ismail Ajjawi · Mariya Rozenblit · Alejandro Rios-Hoyo · Maryam B Lustberg
13 September 2025 at 18:00

Cancers (Basel). 2025 Aug 29;17(17):2831. doi: 10.3390/cancers17172831.

ABSTRACT

Background: Hormone receptor-positive (HR+), HER2-negative breast cancer accounts for the majority of breast cancer diagnoses. While outcomes have improved with neoadjuvant and adjuvant therapies, the risk of late recurrence persists, and there remains a critical need for reliable biomarkers to guide prognosis and post-treatment surveillance. Circulating tumor DNA (ctDNA), detectable via liquid biopsy, has emerged as a promising tool for monitoring minimal residual disease and predicting survival outcomes. This systematic review evaluates the association between ctDNA detection during neoadjuvant or adjuvant treatment and survival outcomes in early-stage HR+/HER2- breast cancer. Methods: This systematic review was conducted in accordance with PRISMA guidelines. A comprehensive literature search of Ovid MEDLINE and Embase was conducted to identify studies published through 3 May 2024 that evaluated ctDNA as a prognostic biomarker in stage I-III HR+/HER2- breast cancer. We included studies reporting recurrence-free survival, invasive disease-free survival, or overall survival and excluded non-original studies, conference abstracts, and non-English articles. Data extraction and qualitative synthesis were performed, and the risk of bias was qualitatively assessed across studies. No review protocol was registered. Results: Eleven studies comprising 1644 patients met the inclusion criteria. In the neoadjuvant setting, ctDNA positivity prior to treatment initiation was associated with inferior survival outcomes. In the adjuvant setting, detection of ctDNA during or after treatment was consistently linked to poorer recurrence-free and invasive disease-free survival. Across studies, ctDNA detection was a significant negative prognostic marker. Conclusions: This systematic review supports the prognostic value of ctDNA in HR+/HER2- early-stage breast cancer. Limitations include small sample sizes, observational study designs, and heterogeneity in ctDNA assays. Standardization of ctDNA testing methods and further prospective trials are needed to validate its clinical utility and explore its potential role in guiding therapeutic interventions.

PMID:40940926 | PMC:PMC12427406 | DOI:10.3390/cancers17172831

Functions of the global health system in a new era

✇Nature Medicine
By: Kumanan Rasanathan · Keith Cloete · Githinji Gitahi · Octavio Gómez-Dantés · Diah Saminarsih · Soumya Swaminathan · Amirhossein Takian · John-Arne Røttingen
11 September 2025 at 08:00

Nature Medicine, Published online: 11 September 2025; doi:10.1038/s41591-025-03936-9

In an irrevocably changed landscape, reform of the global health system needs to answer key questions on functions, what should be delivered in different contexts and at different levels, and how the system should operate.

Interventions Based on Biofeedback Systems to Improve Workers’ Psychological Well-Being, Mental Health, and Safety: Systematic Literature Review

✇Journal of Medical Internet Research
By: Simão Ferreira · Matilde A Rodrigues · Catarina Mateus · Pedro Pereira Rodrigues · Nuno Barbosa Rocha
13 September 2025 at 04:00
Background: In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees’ mental well-being. However, most studies on these interventions have been conducted in controlled laboratory settings. Objective: This review aimed to systematically identify and analyze studies that implemented biofeedback-based interventions in real-world occupational settings, focusing on their effectiveness in improving psychological well-being and mental health. Methods: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed and EBSCO databases for studies published between 2012 and 2024. Inclusion criteria were original peer-reviewed studies that focused on employees and used biofeedback interventions to improve mental health or prevent mental illness. Exclusion criteria included nonemployee samples, lack of a description of the intervention, and low methodological quality (assessed using the Physiotherapy Evidence Database [PEDro] checklist). Data were extracted on study characteristics, intervention type, physiological and self-reported outcomes, and follow-up measures. Risk of bias was assessed, and VOSviewer was used to visualize the distribution of research topics. Results: A total of 9 studies met the inclusion criteria. The interventions used a range of delivery methods, including traditional biofeedback, mobile apps, mindfulness techniques, virtual reality, and cerebral blood flow monitoring. Most studies focused on breathing techniques to regulate physiological responses (eg, heart rate variability and respiratory sinus arrhythmia) and showed reductions in stress, anxiety, and depressive symptoms. Mobile and app-directed interventions appeared particularly promising for improving resilience and facilitating recovery after stress. Of the 9 studies, 8 (89%) reported positive outcomes, with 1 (11%) study showing initial increases in stress due to logistical limitations in biofeedback access. Sample sizes were generally small, and long-term follow-up data were limited. Conclusions: Biofeedback interventions in workplace settings show promising short-term results in reducing stress and improving mental health, particularly when incorporating breathing techniques and user-friendly delivery methods such as mobile apps. However, the field remains underexplored in occupational contexts. Future research should address adherence challenges, scalability, cost-effectiveness, and long-term outcomes to support broader implementation of biofeedback as a sustainable workplace mental health strategy.

Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions

✇ScienceDirect Publication: Artificial Intelligence in Medicine
20 September 2025 at 08:37

Publication date: December 2025

Source: Artificial Intelligence in Medicine, Volume 170

Author(s): Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun, Yu Luo, Ling Wang, Siyuan Wang, Na Guo, Lei Liu, Tian Zhao, Xinzhou Wang, Lei Yang, Shuo Jin, Jun Yan, Jiahong Dong

The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yaman Ayasa · Diyar Alajrami · Mayar Idkedek · Kareem Tahayneh · Firas Abu Akar
13 September 2025 at 18:00

Int J Mol Sci. 2025 Aug 31;26(17):8472. doi: 10.3390/ijms26178472.

ABSTRACT

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption.

PMID:40943394 | PMC:PMC12429163 | DOI:10.3390/ijms26178472

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