Computers in Biology and Medicine, Год журнала: 2024, Номер 172, С. 108152 - 108152
Опубликована: Фев. 13, 2024
Язык: Английский
Information Fusion, Год журнала: 2023, Номер 102, С. 102019 - 102019
Опубликована: Сен. 16, 2023
Emotion recognition is the ability to precisely infer human emotions from numerous sources and modalities using questionnaires, physical signals, physiological signals. Recently, motion has gained attention because of its diverse application areas, like affective computing, healthcare, human–robot interactions, market research. This paper provides a comprehensive systematic review emotion techniques current decade. The includes Physical signals involve speech facial expression, while include electroencephalogram, electrocardiogram, galvanic skin response, eye tracking. an introduction various models, stimuli used for elicitation, background existing automated systems. covers searching scanning well-known datasets followed by design criteria review. After thorough analysis discussion, we selected 142 journal articles PRISMA guidelines. detailed studies available recognition. Our also presented potential challenges in literature directions future
Язык: Английский
Процитировано
132
Information Fusion, Год журнала: 2023, Номер 103, С. 102134 - 102134
Опубликована: Ноя. 10, 2023
Healthcare traditionally relies on single-modality approaches, which limit the information available for medical decisions. However, advancements in technology and availability of diverse data sources have made it feasible to integrate multiple modalities gain a more comprehensive understanding patients' conditions. Multi-modality approaches involve fusing analyzing various types, including images, biosignals, clinical records, other relevant sources. This systematic review provides exploration multi-modality healthcare, with specific focus disease diagnosis prognosis. The adoption healthcare is crucial personalized medicine, as enables profile each patient, considering their genetic makeup, imaging characteristics, history, factors. also discusses technical challenges associated heterogeneous multimodal highlights emergence deep learning powerful paradigm integration.
Язык: Английский
Процитировано
60
Information Fusion, Год журнала: 2024, Номер 108, С. 102412 - 102412
Опубликована: Апрель 6, 2024
Язык: Английский
Процитировано
30
BMJ, Год журнала: 2025, Номер unknown, С. e081554 - e081554
Опубликована: Фев. 5, 2025
Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited clinical practice. This paper describes FUTURE-AI framework, which provides guidance development trustworthy tools healthcare. The Consortium was founded 2021 comprises 117 interdisciplinary experts from 50 countries representing all continents, including scientists, researchers, biomedical ethicists, social scientists. Over a two year period, guideline established through consensus based on six guiding principles—fairness, universality, traceability, usability, robustness, explainability. To operationalise set 30 best practices were defined, addressing technical, clinical, socioethical, legal dimensions. recommendations cover entire lifecycle healthcare AI, design, development, validation to regulation, deployment, monitoring.
Язык: Английский
Процитировано
11
Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 265 - 277
Опубликована: Янв. 1, 2025
Despite the wealth of single-cell multi-omics data, it remains challenging to predict consequences novel genetic and chemical perturbations in human body. It requires knowledge molecular interactions at all biological levels, encompassing disease models humans. Current machine learning methods primarily establish statistical correlations between genotypes phenotypes but struggle identify physiologically significant causal factors, limiting their predictive power. Key challenges modeling include scarcity labeled generalization across different domains, disentangling causation from correlation. In light recent advances data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale framework tackle these issues. This will integrate organism hierarchies, species genotype-environment-phenotype relationships under various conditions. AI inspired by biology may targets, biomarkers, pharmaceutical agents, personalized medicines for presently unmet medical needs.
Язык: Английский
Процитировано
5
Journal of Clinical Medicine, Год журнала: 2025, Номер 14(2), С. 550 - 550
Опубликована: Янв. 16, 2025
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.
Язык: Английский
Процитировано
5
Sensors, Год журнала: 2025, Номер 25(1), С. 205 - 205
Опубликована: Янв. 2, 2025
Objective: In this paper, we explore the correlation between performance reporting and development of inclusive AI solutions for biomedical problems. Our study examines critical aspects bias noise in context medical decision support, aiming to provide actionable solutions. Contributions: A key contribution our work is recognition that measurement processes introduce arising from human data interpretation selection. We concept “noise-bias cascade” explain their interconnected nature. While current models handle well, remains a significant obstacle achieving practical these models. analysis spans entire lifecycle, collection model deployment. Recommendations: To effectively mitigate bias, assert need implement additional measures such as rigorous design; appropriate statistical analysis; transparent reporting; diverse research representation. Furthermore, strongly recommend integration uncertainty during deployment ensure utmost fairness inclusivity. These comprehensive recommendations aim minimize both noise, thereby improving future support systems.
Язык: Английский
Процитировано
2
Mathematics, Год журнала: 2024, Номер 12(5), С. 782 - 782
Опубликована: Март 6, 2024
In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing bibliometrix package RStudio data Web Science, focused on identifying mathematical models evolving role information granulation domain. The research addresses urgent need understand development impact AI within broader scope technological analytical methodologies, particularly concentrating their application banking contexts. bibliometric involved an extensive review literature published during period. We examined key metrics such as annual growth rate, international collaboration, average citations per document, which highlighted field’s expansion collaborative nature. results revealed a significant rate 19.54%, collaboration 21.16%, citation document 25.52. Major journals IEEE Transactions Fuzzy Systems, Sets Journal Intelligent & Information Sciences emerged contributors, aligning with Bradford’s Law’s Zone 1. Notably, post-2020, Systems showed substantial increase publications. A finding was high seminal granulation, emphasizing its importance practical relevance analysis. Keywords like “design”, “model”, “algorithm”, “optimization”, “stabilization”, terms “fuzzy controller”, “adaptive approach” were prevalent. Countries’ Collaboration World Map indicated strong pattern global interconnections, suggesting robust framework collaboration. Our study highlights escalating influence analysis, marked by outputs collaborations. It underscores crucial model sets stage for further investigation into how AI-driven are transforming practices worldwide.
Язык: Английский
Процитировано
12
Information Sciences, Год журнала: 2024, Номер 665, С. 120383 - 120383
Опубликована: Март 3, 2024
This study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using artificial intelligence (AI) models. Despite progress made in mitigating opacity with explainable AI (XAI) and uncertainty quantification (UQ), understanding real-world predictive reliability methods remains a challenge. In this study, we propose novel indicator called Spatial Uncertainty Estimator (SUE) assess prediction classification networks practical Electrocardiography (ECG) scenarios. SUE quantifies spatial overlap critical Grad-CAM (Gradient-weighted Class Activation Mapping) features, offering confidence score predictions. To validate SUE, designed deep learning network that integrates Convolutional Neural Network (CNN) Bidirectional Long Short-Term Memory (BiLSTM) mechanisms precise ECG signal CAD. achieved high accuracy, sensitivity, specificity rates 99.6%, 99.8%, 98.2%, respectively. During test time, accurately distinguishes between correctly classified misclassified segments, demonstrating superiority proposed over existing methods. The highlights potential combining XAI UQ techniques enhance analysis. evaluation among discriminative features provides quantitative insights into network's robustness, encompassing both current accuracy repeatability
Язык: Английский
Процитировано
11
Computer Methods and Programs in Biomedicine, Год журнала: 2024, Номер 254, С. 108253 - 108253
Опубликована: Май 28, 2024
Язык: Английский
Процитировано
11