A review of Explainable Artificial Intelligence in healthcare

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 118, P. 109370 - 109370

Published: June 7, 2024

Language: Английский

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Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(2), P. 550 - 550

Published: Jan. 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.

Language: Английский

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An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

World, Journal Year: 2025, Volume and Issue: 6(1), P. 15 - 15

Published: Jan. 14, 2025

Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical economic challenges. This study develops predictive model for asthma outcomes, leveraging automated machine learning (AutoML) explainable AI (XAI) to balance high accuracy with interpretability. Using comprehensive dataset of demographic, clinical, respiratory function data, we employed AutoGluon automate selection, optimization, ensembling, resulting in 98.99% 0.9996 ROC-AUC score. SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-Agnostic Explanations) were applied provide both global local interpretability, ensuring that clinicians can trust understand predictions. Additionally, counterfactual analysis enabled hypothetical scenario exploration, supporting personalized management by allowing assess potential interventions individual patient risk profiles. To facilitate adoption, Streamlit v1.41.0 application was developed real-time access predictions addresses key gaps prediction, notably transparency generalizability, while providing practical tool enhancing care. Future research could expand the validation across diverse populations reinforce model’s robustness broader environments.

Language: Английский

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Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations

Life, Journal Year: 2025, Volume and Issue: 15(1), P. 94 - 94

Published: Jan. 14, 2025

Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature factors or leverage expanding pool healthcare data. Machine learning (ML) artificial intelligence (AI) approaches represent paradigm shift in prediction, offering dynamic, scalable solutions that integrate diverse data types. This review examines advancements AI/ML for CVD analyzing their strengths, limitations, challenges associated with clinical integration. Recommendations standardization, validation, future research directions are provided unlock potential these technologies transforming precision cardiovascular medicine.

Language: Английский

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Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis

Journal of Healthcare Informatics Research, Journal Year: 2024, Volume and Issue: 8(4), P. 658 - 711

Published: Sept. 14, 2024

Language: Английский

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Interpretation of Artificial Intelligence Models in Healthcare

Journal of Ultrasound in Medicine, Journal Year: 2024, Volume and Issue: 43(10), P. 1789 - 1818

Published: July 19, 2024

Artificial intelligence (AI) models can play a more effective role in managing patients with the explosion of digital health records available healthcare industry. Machine-learning (ML) and deep-learning (DL) techniques are two methods used to develop predictive that serve improve clinical processes These also implemented medical imaging machines empower them an intelligent decision system aid physicians their decisions increase efficiency routine practices. The who going work these need have insight into what happens background how they work. More importantly, be able interpret predictions, assess performance, compare find one best performance fewer errors. This review aims provide accessible overview key evaluation metrics for without AI expertise. In this review, we developed four real-world diagnostic (two ML DL models) breast cancer diagnosis using ultrasound images. Then, 23 most commonly were reviewed uncomplicatedly physicians. Finally, all calculated practically evaluate outputs models. Accessible explanations practical applications effectively interpret, evaluate, optimize ensure safety efficacy when integrated practice.

Language: Английский

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Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

Polymers, Journal Year: 2024, Volume and Issue: 16(23), P. 3368 - 3368

Published: Nov. 29, 2024

The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages production, enable the analysis complex data generated throughout identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due their reliance on variable bio-based feedstocks processing conditions. This review systematically summarizes current applications techniques aiming provide a comprehensive reference for future research while highlighting potential enhance efficiency, reduce costs, improve product quality. also shows role algorithms, including supervised, unsupervised, deep

Language: Английский

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An explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

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Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy

Technologies, Journal Year: 2025, Volume and Issue: 13(2), P. 72 - 72

Published: Feb. 8, 2025

Surgical waiting lists present significant challenges to healthcare systems, particularly in resource-constrained settings where equitable prioritization and efficient resource allocation are critical. We aim address these issues by developing a novel, dynamic, interpretable framework for prioritizing surgical patients. Our methodology integrates machine learning (ML), stochastic simulations, explainable AI (XAI) capture the temporal evolution of dynamic scores, qp(t), while ensuring transparency decision making. Specifically, we employ Light Gradient Boosting Machine (LightGBM) predictive modeling, simulations account variables competitive interactions, SHapley Additive Explanations (SHAPs) interpret model outputs at both global patient-specific levels. hybrid approach demonstrates strong performance using dataset 205 patients from an otorhinolaryngology (ENT) unit high-complexity hospital Chile. The LightGBM achieved mean squared error (MSE) 0.00018 coefficient determination (R2) value 0.96282, underscoring its high accuracy estimating qp(t). Stochastic effectively captured changes, illustrating that Patient 1’s qp(t) increased 0.50 (at t=0) 1.026 t=10) due growth such as severity urgency. SHAP analyses identified (Sever) most influential variable, contributing substantially non-clinical factors, capacity participate family activities (Lfam), exerted moderating influence. Additionally, our achieves reduction times up 26%, demonstrating effectiveness optimizing prioritization. Finally, strategy combines adaptability interpretability, transparent aligns with evolving patient needs constraints.

Language: Английский

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Machine Learning-Driven Predictive Modeling for Lipid Oxidation Stability in Emulsions: A Smart Food Safety Strategy

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104972 - 104972

Published: March 1, 2025

Language: Английский

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