Journal of Medicine Surgery and Public Health, Год журнала: 2024, Номер unknown, С. 100158 - 100158
Опубликована: Ноя. 1, 2024
Язык: Английский
Ageing Research Reviews, Год журнала: 2024, Номер unknown, С. 102497 - 102497
Опубликована: Сен. 1, 2024
Язык: Английский
Процитировано
18
Journal of Medical Systems, Год журнала: 2025, Номер 49(1)
Опубликована: Янв. 16, 2025
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train professionals, and advance medical research. This paper examines various clinical non-clinical applications of Gen AI. In settings, AI supports the creation customized plans, generation synthetic data, analysis images, nursing workflow management, risk prediction, pandemic preparedness, population health management. By automating administrative tasks such as documentations, reduce clinician burnout, freeing more time for direct care. Furthermore, application may surgical outcomes by providing real-time feedback automation certain operating rooms. The data opens new avenues model training diseases simulation, enhancing research capabilities improving predictive accuracy. contexts, improves education, public relations, revenue cycle marketing etc. Its capacity continuous learning adaptation enables it drive ongoing improvements operational efficiencies, making delivery proactive, predictive, precise.
Язык: Английский
Процитировано
5
Medicina, Год журнала: 2025, Номер 61(1), С. 85 - 85
Опубликована: Янв. 7, 2025
Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic management approaches for PH often face limitations, leading to delays in diagnosis potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) deep (DL) offers transformative approach care. Materials Methods: We systematically searched PubMed, Scopus, Web of Science original studies on AI applications PH, using predefined keywords. Out more than 500 initial articles, 45 relevant were selected. Risk bias was evaluated PROBAST (Prediction model Bias Assessment Tool). Results: This review examines the potential focusing its role enhancing diagnosis, disease classification, prognostication. discuss how AI-powered analysis medical data can improve accuracy efficiency detecting PH. Furthermore, we explore risk stratification, optimization Conclusions: While acknowledging existing challenges limitations need continued exploration refinement AI-driven tools, this highlights promise revolutionizing patient
Язык: Английский
Процитировано
4
Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 423 - 439
Опубликована: Янв. 1, 2025
Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised deep reinforcement natural language processing are some main tools used this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, epidemiological for predicting AMR outbreaks. Although relatively new fields, numerous case studies offer substantial evidence their successful application outbreaks with greater accuracy. These provide insights into discovery novel antimicrobials, repurposing existing drugs, combination therapy through analysis molecular structures. In addition, AI-based decision support systems real-time guide healthcare professionals improve prescribing antibiotics. also outlines how AI surveillance, analyze trends, enable early outbreak identification. Challenges, ethical considerations, privacy, model biases exist, however, continuous development methodologies enables play significant combating
Язык: Английский
Процитировано
4
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
3
Academic Radiology, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2
European Journal of Investigation in Health Psychology and Education, Год журнала: 2025, Номер 15(1), С. 6 - 6
Опубликована: Янв. 8, 2025
Artificial intelligence (AI) has transformed healthcare, yet patients' acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack comprehensive research exists that examines the variables driving resistance to AI. This study explores influencing adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, roles need for personal contact (NPC), perceived technological dependence (PTD), general skepticism toward (GSAI) shaping patient integration. For reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews identify adaptable factors healthcare. It then followed survey validate qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The confirm NPC, PTD, GSAI significantly contribute Precisely, who prefer interaction, feel dependent on AI, or are skeptical AI's promises more likely resist adoption. highlight psychological offering valuable insights administrators. Strategies balance efficiency human mitigate dependence, foster trust recommended successful implementation adds theoretical understanding Innovation Resistance Theory, providing both conceptual practical implications effective incorporation
Язык: Английский
Процитировано
2
Frontiers in Digital Health, Год журнала: 2025, Номер 7
Опубликована: Март 7, 2025
Background Advances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, more personalized patient care. However, the direct impact of on clinical outcomes, workflow efficiency, staff well-being requires further elucidation. Methods This integrative review synthesized findings from 18 studies published through November 2024 across diverse settings. Using PRISMA 2020 SPIDER frameworks alongside rigorous quality appraisal tools (MMAT ROBINS-I), examined multifaceted effects integration nursing. Our analysis focused three principal domains: advancements monitoring, efficiency workload management, ethical implications. Results The demonstrates that has yielded substantial benefits. AI-powered monitoring systems, including wearable sensors real-time alert platforms, have enabled nurses to detect subtle physiological changes—such as early fever onset or pain indicators—well before traditional methods, resulting timely interventions reduce complications, shorten hospital stays, lower readmission rates. For example, several reported early-warning algorithms facilitated faster responses, thereby improving safety outcomes. Operationally, AI-based automation routine tasks (e.g., scheduling, administrative documentation, predictive classification) streamlined resource allocation. efficiencies led a measurable reduction nurse burnout job satisfaction, can devote time despite these benefits, challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, potential erosion judgment due overreliance technology. issues underscore need for robust targeted literacy training within curricula. Conclusion holds transformative practice by enhancing both outcomes efficiency. realize benefits fully, it is imperative develop frameworks, incorporate comprehensive education, foster interdisciplinary collaboration. Future longitudinal varied contexts essential validate support sustainable, equitable implementation Policymakers leaders must prioritize investments solutions complement expertise professionals while addressing risks.
Язык: Английский
Процитировано
2
Health care science, Год журнала: 2024, Номер 3(5), С. 329 - 349
Опубликована: Окт. 1, 2024
Abstract The increasing integration of new technologies is driving a fundamental revolution in the healthcare sector. Developments artificial intelligence (AI), machine learning, and big data analytics have completely transformed diagnosis, treatment, care patients. AI‐powered solutions are enhancing efficiency accuracy delivery by demonstrating exceptional skills personalized medicine, early disease detection, predictive analytics. Furthermore, telemedicine remote patient monitoring systems overcome geographical constraints, offering easy accessible services, particularly underserved areas. Wearable technology, Internet Medical Things, sensor empowered individuals to take an active role tracking managing their health. These devices facilitate real‐time collection, enabling preventive care. Additionally, development 3D printing technology has revolutionized medical field production customized prosthetics, implants, anatomical models, significantly impacting surgical planning treatment strategies. Accepting these advancements holds potential create more patient‐centered, efficient system that emphasizes individualized care, better overall health outcomes. This review's novelty lies exploring how radically transforming industry, paving way for effective all. It highlights capacity modern revolutionize addressing long‐standing challenges improving Although approval use digital advanced analysis face scientific regulatory obstacles, they translational research. as continue evolve, poised alter environment, sustainable, efficient, ecosystem future generations. Innovation across multiple fronts will shape revolutionizing provision healthcare, outcomes, equipping both patients professionals with tools make decisions receive treatment. As develop become integrated into standard practices, probably be accessible, effective, than ever before.
Язык: Английский
Процитировано
15
Healthcare, Год журнала: 2024, Номер 12(19), С. 1996 - 1996
Опубликована: Окт. 6, 2024
Healthcare-associated infections are that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome healthcare, can be entirely prevented, and pose a burden terms of financial human costs. With development new AI ML algorithms, hospitals could develop automated surveillance prevention models for HAIs, leading to improved patient safety. The aim this review is systematically retrieve, collect, summarize all available information on application impact HAI and/or prevention.
Язык: Английский
Процитировано
9