Journal of Medical Systems最新文献

Clinical documentation demands necessitate automated solutions balancing clinical precision with patient comprehension. This study aims to develop and validate a unified framework that maintains diagnostic accuracy while dynamically adapting medical report complexity to diverse literacy levels, and to establish comprehensive evaluation methodologies for patient-centered medical documentation. We developed a unified framework integrating three innovations: a hybrid detection method combining CheXFusion and Eigen-CAM for clinical finding detection and anatomical localization; an advanced LLaVA-based pipeline synthesizing clinical predictions with anatomical data for contextually rich medical reports; and a self-reflective large language model system dynamically adapting report complexity across reading levels (6th, 11th, and 18th-grade) while preserving clinical integrity. Our methodology introduces novel evaluation using the Mistral-small model assessing report quality through consistency, coverage, and fluency metrics. Validation on MIMIC-CXR and IU X-Ray datasets demonstrated substantial improvements: 19.78% enhancement in classification accuracy (AUROC), 17.29% improvement in mean average precision, 56.88% increase in patient comprehension scores, and 5.26% gain in diagnostic precision. The framework successfully addresses maintaining clinical rigor while enhancing patient accessibility, reducing documentation burden on healthcare providers and improving patient engagement through comprehensible reporting. This work establishes new standards for automated medical documentation that effectively reconcile clinical precision with patient comprehension in healthcare communication.

Medical misinformation is a major public health concern. The public increasingly uses artificial intelligence (AI) tools for medical consultations. Therefore, concerns arise about their ability to detect and even correct subtle medical information that users may be embedding in users prompts. This study assessed the ability of different ChatGPT models in detecting and correcting such subtle misinformation. Fifty clinical plausible prompts with subtle medical misinformation were introduced separately to ChatGPT models 4o, 4.1-mini, and GPT-5. Prompts spanned Internal Medicine, Cardiology, Pediatrics, Ophthalmology, and Oncology. Responses were scored on a 3-point scale: 0: No correction; 1: Hedging or uncertainty; 3: cutting edge detection and correction. GPT-4o was the best performing model, surpassing GPT-5 by correctly identifying and correcting misinformation in 86% of the prompts compared to 74% for GPT-5. GPT-4.1-mini showed weaker performance, detecting dsmisinformation in only 52% of prompts, with complete failure in 34% and hedging in 14%. Specialty-specific analysis revealed that GPT-4o achieved higher detection rate in all tested specialties compared to GPT-4.1-mini and GPT-5. Only oncology showed comparable detection rates between GPT-4o and GPT-5. Although the performance of GPT-4o and GPT-5 in detecting subtle medical misinformation was promising, unexpectedly, GPT-4o surpassed GPT-5 in performance. Using underpowered variants such as GPT-4.1-mini, poses a public health threat. Reverse prompting offers a diagnostic lens and should be integrated into standard AI safety testing protocols.

A health equity movement is underway, in which broad sectors of society must work together to create solutions to the complex interwoven problems that undermine equal opportunities for good health and well-being. Yet, addressing health inequity is a complex and challenging problem. Health inequity manifests through complex disparities that overload healthcare services and penetrate (all) other sectors of society. The aim of this study is to reach consensus on health equity related topics to be included in European health and social care study programmes by using the Sustainable Development Goals (SDGs). To identify such topics, a Delphi method was designed and performed in an expert panel comprising nine academics, clinicians, and members of a non-governmental organization. Using the Sustainable Development Goals as a framework, three rounds of surveys were conducted. The response rate was 100% across all rounds. In the first round, participants selected relevant SDG targets and indicators; 183 indicators were shortlisted. In the second round, participants rated the relevance of each indicator, leading to the endorsement of 142 indicators. In the third round, 162 out of 247 total indicators were endorsed. None of the Sustainable Development Goals were considered irrelevant to health and social care study programmes. We argue that to address health inequities effectively, health and social care professionals should liaise with a wide range of stakeholders in non-health sectors to design appropriate strategies to improve health and well-being. This implies that health and social care curricula should integrate competencies and capabilities that allow future professionals to work outside their traditional spheres of practice, communicating health information to a broad range of audiences, advocating and translating data for intersectoral action, and negotiating strategies and approaches to attain health equity in collaboration with stakeholders from different social sectors.

Technological advancements are enhancing healthcare, with digital twin (DT) technology emerging as a key tool for personalized and efficient care. This umbrella review systematically evaluates the literature on DT applications in healthcare, focusing on their effectiveness, challenges, and potential to substantially improve patient care.An umbrella review was conducted following the Joanna Briggs Institute (JBI) manual for evidence synthesis and the PRISMA guidelines. A comprehensive literature search was performed across multiple databases, including PubMed, Scopus, Web of Science, and IEEE Xplore, targeting systematic reviews published up to July 2024. The inclusion criteria focused on systematic reviews and meta-analyses related to the usage of DT technologies in healthcare settings.The review identified a considerable number of systematic reviews that highlight the role of DTs in various domains of healthcare, including personalized medicine, predictive maintenance of medical equipment, and healthcare system optimization. Key themes included the integration of real-time data and predictive modeling, which enhance chronic disease management and surgical planning. However, barriers to implementation were noted, including data privacy concerns, validation issues, and high costs.DT technology has the potential to enhance healthcare delivery by enabling personalized treatment and improving operational efficiencies. However, addressing ethical challenges, particularly concerning data privacy and security, is crucial for the successful integration of DTs in clinical practice. This umbrella review underscores the need for continued research to overcome these challenges and facilitate the widespread adoption of DT technologies in healthcare.

Background: The rapid spread of modern outbreaks frequently surpasses the response speed of traditional surveillance and laboratory systems. Mobile apps offer real-time symptom submission, geospatial mapping, and digital contact tracing, which might bridge this gap, yet their epidemiological value and user experience have not been assessed rigorously.

Methods: We developed a novel framework to evaluate utility and usability of surveillance apps. We then demonstrated its use in a proof-of-concept evaluation. An assessment framework with 15 consolidated features was developed from an initial list of 73 identified through the literature and refined by experts. This framework directed the evaluation of available mobile apps for infectious disease surveillance identified via the App Store, Google Play Store, and relevant literature. Two authors applied the criteria independently, and conflicts were panel-resolved (κ = 0.60).

Results: Of the 56 apps screened, 11 met inclusion criteria. Six focused on a single disease, while five tracked multiple diseases. Seven were designed for national use, with four providing global coverage. High-scoring apps combined expert oversight with diverse data sources for broader disease coverage, whereas low performers relied on self-reporting and a single-disease focus. Apps offering user support and customisable alerts scored highest for usability; scores declined when privacy constraints restricted ease of use.

Conclusion: This study presents a structured framework to guide evaluation of mobile apps for epidemic surveillance. The evaluation underscores the need to balance epidemiological functionality with user-friendly design and privacy-conscious features. As mobile apps expand in public health, balancing utility and usability is key to adoption and longevity.

Good mental health is crucial for well-being. Unfortunately, despite the advancements of automatic detection solutions in the mental health field, along with the existence of effective treatments, a large percentage of affected people receive no care for their disorder. Thus, this research proposes an innovative framework integrating counterfactual explanations into a multi-label detection system for anxiety and depression, combining large language models for feature extraction and multi-label machine learning for final prediction. The solution is designed to operate in a streaming mode, addressing the need to process information in real-time. Moreover, sliding window techniques manage the data's evolution, preserving temporal relevance while ensuring robust, user-centered interpretation capabilities. The latter is reinforced by the generation of counterfactual explanations, which contribute to the interpretability, adaptability, and accountability of the results in a critical context, such as mental health. The results surpass the 90% accuracy, indicating very few misclassifications per label. Ultimately, this solution contributes to the literature with timely and transparent decision-making in mental healthcare.

Acute Myeloid Leukemia (AML) is a complex and heterogeneous disease identified by severe clinical progression, fast cellular proliferation, and often high mortality rates. Incorporating diverse longitudinal information on patients' medical histories is essential for developing effective disease predictive models applicable to both research and clinical settings. Here, we present a robust methodology for discovering the regulation of disease progression dynamics from a novel longitudinal, multimodal clinical dataset of patients diagnosed with AML. The medical data were analyzed to reveal the main clinical, genetic, and treatment features modulating disease progression. To discover dynamic mathematical models at the systems level-including the necessary regulatory interactions, parameters, and disease drivers-predictive of AML progression, we developed a de novo inference algorithm based on high-performance evolutionary computation. The results demonstrate that the predictive methodology could accurately estimate the drivers and clinical dynamics of AML progression in terms of blast percentages for both training and novel patients. This approach effectively predicted AML drivers, their mechanistic interactions, and disease progression by leveraging the heterogeneous and longitudinal dynamics of patients' clinical data. Importantly, this methodology shows significant potential for modeling progression dynamics in other acute diseases, providing a flexible and adaptable framework for advancing clinical and translational research.

Meaningful community engagement is an essential component of impactful public health and implementation research. Multiple community engagement methods have been defined, including co-creation. Co-creation involves an iterative process that advances from identifying opportunities for value creation and solutions, to defining partner priorities, to evaluating co-created outcomes. This study reports our methods to co-create culturally and linguistically meaningful mHealth messages to promote preventive healthcare engagement for Arabic, Spanish, and Vietnamese - speaking communities. This multi-method study is part of a larger program of research, "Working towards Empowered community-driven Approaches to increase Vaccination and preventive care Engagement" (WEAVE), that aims to co-create and test a preventive healthcare program that includes mHealth and care coordination with medically underserved patients at multiple federally qualified health center (FQHC) locations near the US/Mexico border and surrounding region. A multi-level partner process was used to engage in co-creation across six partner groups (n = 27): (1) Community Advisory Boards (CAB), (2) Community Weavers (individuals with lived experience as members of an underserved community who act as cultural brokers between communities, public health systems, and researchers), (3) FQHC Care Coordinators, (4) FQHC Administrators, (5) a FQHC Clinical Expert, and (6) Research Experts in health communication, vaccine behavior research, and/or mHealth. Each of these partner groups was distinctly engaged through structured CAB meetings, weekly research and operations team meetings, topic-specific meetings, and e-review of content. The Research Engagement Survey Tool (REST) was used as a global assessment of partner engagement in the co-creation process. Results are organized by a co-creation framework anchored to identify, analyze, define, and design steps. Across four CAB meetings and engagement activities with the other co-creation partners, 262 mHealth messages (89 Arabic, 85 Spanish, 88 Vietnamese) were refined and approved. A message cadence and delivery mode were finalized. On the REST, the average ratings were over 4.50 (out of 5), indicating strong perceptions of engagement with the co-creation process and members. We successfully engaged six co-creation partner groups to develop and approve the content, cadence, and delivery mode of mHealth preventive care messages. These messages will be embedded in the multicomponent health program that will be tested in a randomized adaptive trial. NCT05841810, registration date: 03/28/2023.

Objective: To develop a tool for the clinical hybrid imaging workflow which combines morphologic and functional measurements. And to quantify the number of clicks saved per positron emission tomography/computed tomography (PET/CT) interpretation.

Methods: A tool was developed where a volume of interest (VOI) is automatically created around line distance measurements. VOI statistics for both PET and CT component, and line distances are generated and displayed. Usage data for the first two months after introduction of the tool was analyzed.

Results: Eleven radiologists and nuclear medicine physicians used the tool in 364 PET/CTs. In 19% of examinations, the novel tool was the only tool that needed to be used. The novel combined tool was used 1001 times, whereas the traditional spherical VOI had been placed 1131 times. The usage ratio of new to traditional tool differed significantly between examinations with ≤ 6 annotations (ratio 1.0) versus > 6 annotations (ratio 0.63, p = 0.030). The average number of saved clicks per PET/CT was estimated at 16.5.

Conclusion: A novel combined measurement tool for hybrid imaging was implemented and saved on average 16.5 clicks per examination. These improvements contribute to a smoother workflow and demonstrate the positive impact of thoughtful software design in medical practice.

While Fine needle aspiration cytology (FNAC) and mammography are both used to diagnose breast lesions, FNAC is generally more accurate than mammograms for predicting breast cancer. It is also gaining popularity as an early detection tool due to its rapid and straightforward procedure, cost-effectiveness, and minimal risk of complications. Deep learning enhances breast cancer detection by extracting crucial features, yielding highly accurate results compared to conventional techniques. Classical machine learning is less time-intensive and requires fewer parameter adjustments. This work is presented as a proof-of-concept study on FNAC images obtained from two centers. It explores eighteen hybrid architectures that are developed and evaluated, combining the strength of deep learning techniques- Inception-V3, MobileNet-V2, and DenseNet-121 for feature extraction, with three machine learning classifiers (Support Vector Machine, Decision Tree, and k-Nearest Neighbours) for binary classification of fine needle aspiration cytology images of the breast. Our study is based on an indigenously collected dataset of 427 images (152 benign and 275 malignant), which was later expanded through augmentation to 2,866 images (1216 benign and 1,650 malignant). The hybrid model, which combines feature extraction from MobileNet-V2 and DenseNet-121 in a concatenated architecture, achieves the highest internal test accuracy of 98.26% when paired with an SVM classifier. It also achieves the best-known sensitivity (97.95%) and specificity (98.48%). The explainability model, which utilizes Grad-CAM, achieved 95% positive clinical validation by expert pathologists, underscoring the model's trustworthiness and interpretability-critical for clinical adoption and decision-making support. The proposed hybrid model, with its impressive metrics and validation rate, underscores the model's ability to provide clear, interpretable insights that support clinical decision-making.