Hasil untuk "Computer applications to medicine. Medical informatics"

Joseph A. Cruz, D. Wishart

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Peng Liu, Lin Lu, Jiayao Zhang et al.

Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

Qingming Wu, Yubin Wang, Lili Lu et al.

Virtual simulation (VS) as an emerging interactive pedagogical strategy has been paid more and more attentions in the undergraduate medical education. Because of the fast development of modern computer simulation technologies, more and more advanced and emerging VS-based instructional practices are constantly increasing to promote medical education in diverse forms. In order to describe an overview of the current trends in VS-based medical teaching and learning, this scoping review presented a worldwide analysis of 92 recently published articles of VS in the undergraduate medical teaching and learning. The results indicated that 98% of included articles were from Europe, North America, and Asia, suggesting a possible inequity in digital medical education. Half (52%) studies reported the immersive virtual reality (VR) application. Evidence for educational effectiveness of VS in medical students’ knowledge or skills was sufficient as per Kirkpatrick’s model of outcome evaluation. Recently, VS has been widely integrated in surgical procedural training, emergency and pediatric emergency medicine training, teaching of basic medical sciences, medical radiation and imaging, puncture or catheterization training, interprofessional medical education, and other case-based learning experiences. Some challenges, such as accessibility of VS instructional resources, lack of infrastructure, “decoupling” users from reality, as well as how to increase students’ motivation and engagement, should be addressed.

Nicki Lentz-Nielsen, Lars Maaløe, Pascal Madeleine et al.

<b>Background:</b> Chronic obstructive pulmonary disease (COPD) is projected to be the third-leading cause of death by 2030. Traditional spirometry for the monitoring of the forced expiratory volume in one second (FEV1) can provoke discomfort and anxiety. This study aimed to validate AI models using daily audio recordings as an alternative for FEV1 estimation in home settings. <b>Methods</b>: Twenty-three participants with moderate to severe COPD recorded daily audio readings of standardized texts and measured their FEV1 using spirometry over nine months. Participants also recorded biomarkers (heart rate, temperature, oxygen saturation) via tablet application. Various machine learning models were trained using acoustic features extracted from 2053 recordings, with K-nearest neighbor, random forest, XGBoost, and linear models evaluated using 10-fold cross-validation. <b>Results:</b> The K-nearest neighbors model achieved a root mean square error of 174 mL/s on the validation data. The limit of agreement (LoA) ranged from −333.21 to 347.26 mL/s. Despite an error range of −1252 to 1435 mL/s, most predictions fell within the LoA, indicating good performance in estimating the FEV1. <b>Conclusions</b>: The predictive model showed promising results, with a narrower LoA compared to traditional unsupervised spirometry methods. The AI models effectively used audio to predict the FEV1, suggesting a viable non-invasive approach for COPD monitoring that could enhance patient comfort and accessibility in home settings.

Jianfeng Liu, Wei Xing, Xingyang Zhang et al.

Peng Wang

Abstract Background The annotation of protein functions constitutes a key connection between genetic sequences, molecular conformations, and biochemical roles, driving progress in biomedical studies. Traditional experimental methods are time-consuming and resource-intensive, making it difficult to meet the demand for functional annotation of a vast number of proteins in the post-genomic era. The development of high-throughput sequencing technology has generated a large amount of protein-protein interaction (PPI) data. Prediction methods based on network topology have attracted attention due to their high efficiency and interpretability. The FSWeight algorithm calculates functional similarity by evaluating the commonality of second-order neighbors of proteins. However, it has limitations in terms of insufficient local information and a limited global perspective. Results In this study, we propose the topology-aware functional similarity (TAFS) framework, which integrates local neighborhood information with global topological information. A distance-dependent functional attenuation factor $$\gamma $$ is introduced to dynamically adjust the weights of distant nodes, and a bidirectional joint co-function probability model is constructed. Experiments show that TAFS outperforms traditional baseline methods in both single-species and cross-species evaluations. Conclusion TAFS significantly improves prediction accuracy and interpretability through refined topological modeling, providing new insights for functional inference in complex biological networks.

Dena Trujillo

Robert A. Rigby, Mikis D. Stasinopoulos, Achim Zeileis et al.

We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The author compares a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which is the standard adopted by the Global Lung Function Initiative (GLI), with a segmented linear regression (SLR) model, for pulmonary function variables. The author presents an interesting comparison; however there are some fundamental issues with the approach. We welcome this opportunity for discussion of the issues that it raises. The author's contention is that (1) SLR provides "prediction accuracies on par with GAMLSS"; and (2) the GAMLSS model equations are "complicated and require supplementary spline tables", whereas the SLR is "more straightforward, parsimonious, and accessible to a broader audience". We respectfully disagree with both of these points.

Joshua Hatherley

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.

Felix Buendía, Joaquín Gayoso-Cabada, José-Luis Sierra

In this paper, we describe an approach to transforming the huge amount of medical knowledge available in existing online medical collections into standardized learning packages ready to be integrated into the most popular e-learning platforms. The core of our approach is a tool called Clavy, which makes it possible to retrieve pieces of content in medical collections, to transform this content into meaningful learning units, and to export it in the form of standardized learning packages. In addition to describing the approach, we demonstrate its feasibility by applying it to the generation of IMS content packages from MedPix, a popular online database of medical cases in the domain of radiology.

Xiaowen Tao, Yinuo Wang, Jinzhao Zhou

End-to-end reinforcement learning (RL) for motion control trains policies directly from sensor inputs to motor commands, enabling unified controllers for different robots and tasks. However, most existing methods are either blind (proprioception-only) or rely on fusion backbones with unfavorable compute-memory trade-offs. Recurrent controllers struggle with long-horizon credit assignment, and Transformer-based fusion incurs quadratic cost in token length, limiting temporal and spatial context. We present a vision-driven cross-modal RL framework built on SSD-Mamba2, a selective state-space backbone that applies state-space duality (SSD) to enable both recurrent and convolutional scanning with hardware-aware streaming and near-linear scaling. Proprioceptive states and exteroceptive observations (e.g., depth tokens) are encoded into compact tokens and fused by stacked SSD-Mamba2 layers. The selective state-space updates retain long-range dependencies with markedly lower latency and memory use than quadratic self-attention, enabling longer look-ahead, higher token resolution, and stable training under limited compute. Policies are trained end-to-end under curricula that randomize terrain and appearance and progressively increase scene complexity. A compact, state-centric reward balances task progress, energy efficiency, and safety. Across diverse motion-control scenarios, our approach consistently surpasses strong state-of-the-art baselines in return, safety (collisions and falls), and sample efficiency, while converging faster at the same compute budget. These results suggest that SSD-Mamba2 provides a practical fusion backbone for resource-constrained robotic and autonomous systems in engineering informatics applications.

Simrata Bindra, Richa Jain

Syaron Basnet, Michael Chaiton

BackgroundThe Wellness Together Canada (WTC) portal is a digital mental health intervention that was developed in response to an unprecedented rise in mental health and substance use concerns due to the COVID-19 pandemic, with funding from the Government of Canada. It is a mental health and substance use website to support people across Canada providing digital interventions and services at no cost. Two million people have visited the WTC portal over the course of 1 year since launching; however, rigorous evaluation of this potential solution to access to mental health care during and after the COVID-19 pandemic is urgently required. ObjectiveThis study aims to better understand the effectiveness of the existing digital interventions in improving population mental health in Canada. MethodsThe Let’s Act on Mental Health study is designed as a longitudinal fully remote, equally randomized (1:1), double-blind, alternative intervention–controlled, parallel-group randomized controlled trial to be conducted between October 2023 and April 2024 with a prospective follow-up study period of 26 weeks. This trial will evaluate whether a digital intervention such as the WTC improves population mental health trajectories over time. ResultsThe study was approved by the research ethics board of CAMH (Centre for Addiction and Mental Health, Toronto, Ontario, Canada). It is ongoing and participant recruitment is underway. As of August 2023, a total of 453 participants in the age group of 18-72 years have participated, of whom 70% (n=359) are female. ConclusionsThis initiative provides a unique opportunity to match people’s specific unmet mental health and substance use needs to evidence-based digital interventions.

Pengcheng Chen, Jin Ye, Guoan Wang et al.

Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited clinical relevance, incomplete evaluations, and insufficient guidance for interactive LVLMs. To address these limitations, we developed the GMAI-MMBench, the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format. Additionally, we implemented a lexical tree structure that allows users to customize evaluation tasks, accommodating various assessment needs and substantially supporting medical AI research and applications. We evaluated 50 LVLMs, and the results show that even the advanced GPT-4o only achieves an accuracy of 53.96%, indicating significant room for improvement. Moreover, we identified five key insufficiencies in current cutting-edge LVLMs that need to be addressed to advance the development of better medical applications. We believe that GMAI-MMBench will stimulate the community to build the next generation of LVLMs toward GMAI.

Jebran Khan, Kashif Ahmad, Senthil Kumar Jagatheesaperumal et al.

In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted approach involving all the stakeholders, sustainable planning, efficient resource management, innovative solutions, and modern technologies. Like other modern technologies, social media informatics also plays its part in developing more sustainable and resilient cities and societies. Despite its limitations, social media informatics has proven very effective in various sustainable cities and society applications. In this paper, we review and analyze the role of social media informatics in sustainable cities and society by providing a detailed overview of its applications, associated challenges, and potential solutions. This work is expected to provide a baseline for future research in the domain.

Zhiyuan Wang, Jinhao Duan, Chenxi Yuan et al.

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications.

Qiang Qiao, Wenyu Wang, Meixia Qu et al.

The field of medical image segmentation is challenged by domain generalization (DG) due to domain shifts in clinical datasets. The DG challenge is exacerbated by the scarcity of medical data and privacy concerns. Traditional single-source domain generalization (SSDG) methods primarily rely on stacking data augmentation techniques to minimize domain discrepancies. In this paper, we propose Random Amplitude Spectrum Synthesis (RASS) as a training augmentation for medical images. RASS enhances model generalization by simulating distribution changes from a frequency perspective. This strategy introduces variability by applying amplitude-dependent perturbations to ensure broad coverage of potential domain variations. Furthermore, we propose random mask shuffle and reconstruction components, which can enhance the ability of the backbone to process structural information and increase resilience intra- and cross-domain changes. The proposed Random Amplitude Spectrum Synthesis for Single-Source Domain Generalization (RAS^4DG) is validated on 3D fetal brain images and 2D fundus photography, and achieves an improved DG segmentation performance compared to other SSDG models.

Lie Cai, Andre Pfob

Background: Using artificial intelligence (AI) techniques for computational medical image analysis has shown promising results. However, colored images are often not readily available for AI analysis because of different coloring thresholds used across centers and physicians as well as the removal of clinical annotations. We aimed to develop an open-source tool that can adapt different color thresholds of HSV-colored medical images and remove annotations with a simple click. Materials and Methods: We built a function using MATLAB and used multi-center international shear wave elastography data (NCT 02638935) to test the function. We provide step-by-step instructions with accompanying code lines. Results: We demonstrate that the newly developed pre-processing function successfully removed letters and adapted different color thresholds of HSV-colored medical images. Conclusion: We developed an open-source tool for removing letters and adapting different color thresholds in HSV-colored medical images. We hope this contributes to advancing medical image processing for developing robust computational imaging algorithms using diverse multi-center big data. The open-source Matlab tool is available at https://github.com/cailiemed/image-threshold-adapting.

H. Alzghaibi

Background In the field of health informatics (HI), there is a crucial gap between employers’ needs and the output of academic programmes. Although industrial organisations and government agencies recognise the importance of training and education in the development and operation of health-information systems, advancements in educational programmes have been comparatively slow in terms of investment in healthcare information technology. This study aims to determine the gap between employer demands and academic programmes in HI in Saudi Arabia. Methods This mixed-methods study collected both qualitative and quantitative data. A qualitative content analysis was performed to identify the role of advertised HI jobs using two sources: Google and LinkedIn. In addition, university websites were searched to determine job opportunities for graduates with a bachelor’s degree in HI. Next, a quantitative, cross-sectional self-report questionnaire was administered to validate the findings of the qualitative data. Data obtained were analysed using SPSS, N-Vivo, and Microsoft Excel. Results The study’s data were obtained from four sources: Google search engine, LinkedIn, five Saudi university websites, and 127 HI experts. The results show a discrepancy between academic programmes’ outputs and employer recruitment needs. In addition, the results reveal a preference for post-graduate degrees, either a master’s or PhD degree, with a bachelor’s degree in a health or medical discipline. Conclusions Employers tend to prefer applicants with a bachelor’s degree in computer science or information technology over those with a degree in HI. Academic programmes should incorporate more practical applications and provide students with a thorough understanding of the healthcare industry to better equip them as efficient future HI professionals.

J. Sim, Q. Fong, Weimin Huang et al.

With the advent of artificial intelligence (AI), machines are increasingly being used to complete complicated tasks, yielding remarkable results. Machine learning (ML) is the most relevant subset of AI in medicine, which will soon become an integral part of our everyday practice. Therefore, physicians should acquaint themselves with ML and AI, and their role as an enabler rather than a competitor. Herein, we introduce basic concepts and terms used in AI and ML, and aim to demystify commonly used AI/ML algorithms such as learning methods including neural networks/deep learning, decision tree and application domain in computer vision and natural language processing through specific examples. We discuss how machines are already being used to augment the physician's decision-making process, and postulate the potential impact of ML on medical practice and medical research based on its current capabilities and known limitations. Moreover, we discuss the feasibility of full machine autonomy in medicine.