Journal of Medical Systems最新文献

The recent study by Wu et al. (2025) comparing DeepSeek-R1 and ChatGPT-4o on the Chinese National Medical Licensing Examination (CNMLE) provides an important contribution to understanding large language model (LLM) performance in non-English medical contexts. While their findings highlight the potential of LLMs in medical knowledge assessment, several methodological issues merit further discussion. First, the exclusive use of Chinese-language items without bilingual comparison may favor DeepSeek-R1, which demonstrates strong performance in Chinese, over ChatGPT-4o, whose training corpus is predominantly English-based. Second, the evaluation was conducted before the release of GPT-5, leading to potential disparities in reasoning capabilities between models. Third, the restriction to multiple-choice questions limits the assessment to factual recall rather than higher-order reasoning or clinical judgment. We commend the authors for initiating this valuable cross-linguistic analysis and suggest that future studies incorporate bilingual testing, ensure model functional parity, and include open-ended clinical items to more comprehensively evaluate LLMs' reasoning and interpretive competence in real-world medical education contexts.

OpenAI's GPT-5 demonstration showed a patient uploading pathology reports to guide treatment decisions, though privacy implications were not addressed. We evaluated GPT-5 against 100 gastrointestinal oncology cases with tumor-board validation and found identical 85% concordance to GPT-4o, contradicting superiority claims. We recommend mandatory accuracy disclosures and regulatory oversight for AI health demonstrations to protect patient safety and privacy.

In recent years, progress in artificial intelligence, particularly in the realm of deep learning, has resulted in substantial enhancements in the diagnosis of various medical conditions. This study introduces a framework that leverages multiple lightweight deep learning models to assess their effectiveness in analyzing raw lung auscultation sounds - no feature engineering or preprocessing - to detect eleven different respiratory pathologies. The objective was to enhance the accuracy of respiratory disease diagnoses and conduct a comparative analysis of these models to pinpoint the most efficient model. The models were assessed based on their performance across two distinct datasets, one in its original form and the other after augmentation. The outcomes underscore the successful utilization of the deep learning framework, because it achieves remarkable accuracy in the detection of respiratory pathologies through the analysis of raw lung sounds alone. Furthermore, all the deep learning models proposed in the framework exhibited accuracy rates exceeding 99%, with the hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model, which combines CNN for feature extraction and LSTM for temporal modeling, emerging as the top performer across all datasets. The augmentation process was also proven to be effective, leading to performance enhancements in deep-learning models. Finally, the lightweight hybrid CNN-LSTM model, which is less complex with only 15 layers, outperformed the standalone CNN and LSTM architectures, achieving up to 100% accuracy on the augmented dataset. These results suggest that raw auscultation sounds can be used to reliably detect multiple respiratory pathologies using lightweight and deployable deep learning models. The reported performance metrics reflect in-dataset evaluation only, and external validation on data from additional clinical datasets will be required to assess generalization.

Bone age assessment and adult height prediction are essential for evaluating pediatric growth. Traditional methods rely on manual radiographic interpretation, which is subjective, time-consuming, and prone to inter-observer variability. This study presents an automated approach using a cascaded deep learning model to assess bone age and predict adult height from pediatric hand radiographs, aiming to improve diagnostic objectivity and efficiency. A total of 8,242 left-hand radiographs from Chinese children were retrospectively collected. Bone age was annotated by experienced pediatric endocrinologists using the China-05 standard. The model employed Yolact for instance segmentation to detect and classify bone structures, followed by parallel ResNet-18 subnetworks to grade ossification centers in the radius, ulna, and metacarpal/phalangeal bones. Predicted grades were integrated using a standardized scoring system to estimate bone age. A regression model then predicted adult height based on these features. The model achieved a Pearson correlation of 0.98 ([Formula: see text]) for bone age and 0.94 ([Formula: see text]) for adult height predictions. Bland-Altman analysis showed minimal bias and narrow limits of agreement. Mean absolute errors were 0.25 years for bone age and 1.75 cm for adult height. Average inference time was 7.8 seconds, significantly enhancing clinical efficiency. The proposed cascaded deep learning model delivers accurate, efficient, and reliable bone age assessment and adult height prediction, offering strong potential for clinical integration in pediatric growth evaluation.

The Data Management and Sharing (DMS) Policy issued by the National Institutes of Health (NIH) requires most grant applications to include a DMS Plan, detailing data type(s), resources (e.g., data repositories, knowledgebases, portals) for data sharing, and a dissemination timeline. Researchers face challenges navigating the complex data landscape to identify data resources to fulfill the DMS Policy requirements. The National Institute of Allergy and Infectious Diseases (NIAID) aims to support researchers in preparing DMS Plans for applications that align with its mission areas. To support depositing and accessing infectious, allergic, and immune-mediated disease (IID) data, we compiled a list of IID data resources. The list was developed by reviewing online resources and collecting recommendations from subject matter experts. Additionally, we developed a questionnaire based on NIH recommendations and community best practices to characterize a subset of IID data resources that support data submissions. We identified 303 data resources, 58 of which focused on IID data. Most were categorized as General Infectious Diseases and Pathogens (n = 29, 50%), followed by Respiratory Pathogens (n = 10, 17%). Scientific content included "omics" (n = 37, 64%), clinical (n = 21, 36%), and biological assay data (n = 20, 34%). Open access data was common (n = 39, 67%), with fewer offering controlled access (n = 20, 34%) or required registration (n = 4, 7%). Among 19 resources accepting data submissions, eight (42%) required registration, seven (37%) needed additional approvals, and four (21%) required network membership. Fifteen (79%) resources provided metadata access, with 11 (58%) assigning persistent identifiers. Twelve (63%) offered APIs, 13 (68%) provided analytical tools, and 10 (53%) featured workspaces. Risk management documentation was available for 10 (53%), and five (26%) provided data retention policies. We assessed 58 data resources in the IID domain, identifying 19 that support data submission and are therefore suitable for NIH DMS Plans. Our findings reveal both the breadth of available resources, and the challenges related to inconsistent data submission requirements and data management practices. Enhancing transparency and standardization across data resources will support more effective data sharing, enhance findability, and aid researchers in selecting appropriate resources for DMS Plans and secondary data analysis.

While artificial intelligence (AI) has demonstrated potential in automating clinical trial matching, most existing solutions rely on high-level structured data or oversimplified criteria. This study introduces a framework to structure and analyze eligibility criteria across three real-world trial protocols, aiming to inform more granular AI-driven trial matching strategies. Trial criteria from three protocols were decomposed into individual variables and evaluated based on data type, scope, and dependency. Complexity was assessed using a novel formula incorporating the number of independent and dependent variables, alongside the Flesch-Kincaid reading grade level. Quantitative analysis explored variation across trials. Protocols contained between 22-160 eligibility variables, with 4-22% showing interdependence. Reading grade levels ranged from sixth grade to first-year college. Complexity scores varied significantly, with some trials exhibiting particularly high cognitive and logical burdens. Recursive and hierarchical structures were prevalent in high-complexity protocols. This study reveals the substantial variability and structural complexity of clinical trial criteria, highlighting challenges for AI matching systems. A standardized approach to measuring trial complexity can enhance algorithm transparency, scalability, and interpretability. These findings underscore the need for structured, computable frameworks to improve equity and efficiency in clinical trial recruitment.

The Nuclear Medicine Scheduling problem consists of assigning patients to a day, on which the patient will undergo the medical check, the preparation, and the actual image detection process. The schedule of the patients should consider their different requirements and the available resources, e.g., varying time required for different diseases and radiopharmaceuticals used, number of injection chairs, and tomographs available. Recently, this problem has been solved using a logic-based approach using the Answer Set Programming (ASP) methodology. However, it may be the case that a computed schedule can not be implemented due to a sudden emergency and/or unavailability of resources, thus rescheduling is needed. In this paper, we present an ASP-based approach to solve such a situation, which we call the Nuclear Medicine Rescheduling problem. Experiments on three scenarios in which rescheduling may be needed, and employing real data from a medium size hospital in Italy, show that our rescheduling solution provides satisfying results even when the concurrent number of emergencies and unavailability is significant. We finally present the design and implementation of a web application for the easy usage of our solutions.

Early diagnosis of osteoarthritis (OA) remains a critical unmet need due to the lack of reliable detection methods. Detecting OA at an early stage provides a valuable clinical window for implementing effective intervention strategies. Raman spectroscopy (RS) holds promise for improving predictive accuracy in detecting osteoarthritic changes at the molecular level, monitoring disease progression, and assessing severity. This study aimed to systematically evaluate the predictive performance of RS in OA assessment in human samples, thereby highlighting current advancements in the field. The search included PubMed/Medline, Scopus, Web of Science, and IEEE for studies published up to July 31, 2024. Two authors individually screened the studies using Covidence software, and data extraction was based on predefined criteria. The Prediction Model Risk of Bias Assessment Tool was employed to evaluate the bias and applicability of the included studies. Ten studies met the inclusion criteria. Near-infrared excited RS was the most used RS technique. All included studies reported predictive accuracy ranging from 73% to 100% in preclinical settings for OA assessment. Although all studies performed internal validation, most had a high risk of bias and none reported external validation, which limits the generalizability of their findings. These findings underscore both the potential and current limitations of RS in OA assessment. Future research should prioritize larger sample sizes, external validation, and standardized RS protocols to improve reproducibility across diverse clinical settings.