Computer Vision in Clinical Neurology

JAMA Neurology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Importance Neurological examinations traditionally rely on visual analysis of physical clinical signs, such as tremor, ataxia, or nystagmus. Contemporary score-based assessments aim to standardize and quantify these observations, but tools suffer from clinimetric limitations often fail capture subtle yet important aspects human movement. This poses a significant roadblock more precise personalized neurological care, which increasingly focuses early stages disease. Computer vision, branch artificial intelligence, has the potential address challenges by providing objective measures signs based solely video footage. Observations Recent studies highlight computer vision measure disease severity, discover novel biomarkers, characterize therapeutic outcomes in neurology with high accuracy granularity. may enable sensitive detection movement patterns that escape eye, aligning an emerging research focus stages. However, accessibility, ethics, validation need be addressed for widespread adoption. In particular, improvements usability algorithmic robustness are key priorities future developments. Conclusions Relevance technologies have revolutionize practice objective, quantitative signs. These could enhance diagnostic accuracy, improve treatment monitoring, democratize specialized care. Clinicians should aware their complement traditional assessment methods. further focusing validation, ethical considerations, practical implementation is necessary fully realize neurology.

Language: Английский

---

AI in diagnostics: Enhancing accuracy and efficiency

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 279 - 304

Published: Jan. 1, 2025

Language: Английский

---

Use of Natural Language Processing and Computer Vision in Deep Learning for Equipment Failure Investigation on Drilling Tools

International Petroleum Technology Conference, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract Incident investigation analysis within the oil and gas industry is a critical process to ensure operational safety, minimize downtime, improve asset management. However, sheer volume heterogeneous nature of data sources (including structured unstructured text visual information) present significant challenges traditional methods incident classification contextual understanding are labor-intensive error-prone. This paper addresses these by proposing an approach that harnesses natural language processing (NLP) computer vision techniques in deep learning for equipment failure drilling tools. The first component our focuses on leveraging NLP automated from mixture industry. With vast volumes generated maintenance logs, technician reports, summaries, manual becomes impractical By applying advanced algorithms, including mining sentiment analysis, we automate categorizing incidents, enabling real-time prioritization deeper semantic analysis. second introduces novel application vision, where employ learning-based detect extract textual information images captured various electronic boards. training models annotated image datasets, methodology facilitates extraction content diverse boards, enriching with valuable insights. Our analyzes enables rapid identification, categorization, incidents. automating detection board sources, model built this study enhances collection, improves context understanding, efficient extraction, more accurate root cause Through empirical validation case studies, demonstrate efficacy novelty integrated approach. streamlines providing insights into contexts, informed decision-making. scalable effective solution response, preserves integrity sector, offering transformative complex challenges.

Language: Английский

---

Continuous patient monitoring with AI: real-time analysis of video in hospital care settings

Frontiers in Imaging, Journal Year: 2025, Volume and Issue: 4

Published: March 10, 2025

Introduction This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings, developed by LookDeep Health. Leveraging advanced computer vision, the provides real-time insights into behavior interactions through video analysis, securely storing inference results cloud retrospective evaluation. Methods The AI system detects key components rooms, including individuals' presence roles, furniture location, motion magnitude, boundary crossings. Inference are stored dataset, compiled with 11 partners, includes over 300 high-risk fall patients spans more than 1,000 days of inference. An anonymized subset is publicly available to foster innovation reproducibility at lookdeep/ai-norms-2024 . Results Performance evaluation demonstrates strong accuracy object detection (macro F1-score = 0.92) patient-role classification (F1-score 0.98). reliably tracks “patient alone” metric (mean logistic regression 0.82 ± 0.15), enabling isolation, wandering, unsupervised movement-key indicators risk adverse events. Discussion work establishes benchmarks monitoring, highlighting platform's potential enhance safety continuous, data-driven interactions.

Language: Английский

---

Computer Vision to Enhance Healthcare Domain: An Overview of Features, Implementation, and Opportunities

Intelligent Pharmacy, Journal Year: 2024, Volume and Issue: 2(6), P. 792 - 803

Published: May 21, 2024

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one growing modern AI technologies. distribution and administration medications are about change by using CV for medication management. This system scans pharmaceutical labels keeps track process from delivery cameras, sensors, computer algorithms. In order assure accuracy in medicine dose, also makes it easier doctors, nurses, chemists communicate. vision-driven management can significantly lower number medical mistakes that result inaccurate or missing prescriptions, improper doses, simply forgetting take a particular drug. An exhaustive literature review been done identify work related research objectives. paper their need healthcare. Various tasks associated with domain discussed. Targeted goals through traits briefed. Finally, significant applications CVs were identified Nowadays, practical uses Its methods widely used since they have shown excellent utility contexts, including imaging surgical planning. study how program computers comprehend digital pictures. Numerous utilise this technology, such as automated abnormality identification, illness diagnosis, procedure guiding. expanding quickly enormous promise enhance Some many sector include patient identification systems, picture analysis, simulation diagnosis.

Language: Английский

---

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety

Archives of Public Health, Journal Year: 2024, Volume and Issue: 82(1)

Published: Oct. 23, 2024

The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) provide care that is consistent with patients' needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability predict and recommend best course of for prospective patients left chance. This review maps evidence between January 1, 2010 December 31, 2023, perceived threats posed by usage tools healthcare rights safety. We deployed guidelines Tricco et al. conduct a comprehensive search current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, Google Scholar. In all, 80 peer reviewed articles qualified were included this study. report there real chance unpredictable errors, inadequate policy regulatory regime use technologies healthcare. Moreover, medical paternalism, increased cost disparities insurance coverage, data security privacy concerns, bias discriminatory services imminent Our findings have some critical implications achieving Sustainable Development Goals (SDGs) 3.8, 11.7, 16. national governments should lead roll-out systems. Also, other key actors industry contribute developing policies

Language: Английский

---

A survey on comparative study of lung nodules applying machine learning and deep learning techniques

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 20, 2024

Language: Английский

---

Revolutionizing Patient Safety: The Economic and Clinical Impact of Artificial Intelligence in Hospitals

Hospitals, Journal Year: 2024, Volume and Issue: 1(2), P. 185 - 194

Published: Dec. 12, 2024

Artificial intelligence (AI) has emerged as a transformative force in enhancing patient safety within hospital settings. This perspective explores the various applications of AI improving outcomes, including early warning systems, predictive analytics, process automation, and personalized treatment. We also highlight economic benefits associated with implementation, such cost savings through reduced adverse events improved operational efficiency. Moreover, addresses how can enhance pharmacological treatments, optimize diagnostic testing, mitigate hospital-acquired infections. Despite promising advancements, challenges related to data quality, ethical concerns, clinical integration remain. Future research directions are proposed address these harness full potential healthcare.

Language: Английский

---

Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 141064 - 141087

Published: Jan. 1, 2024

Language: Английский

---

Ethical and Practical Dimensions of Artificial Intelligence (AI) in Healthcare: A Comprehensive Study of Professional Perceptions

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Introduction Artificial intelligence (AI) transforms medicine by enhancing diagnoses, treatments, resource management, and personalized treatment plans. However, it poses ethical legal challenges, such as data privacy equitable access to its benefits. This study seeks understand healthcare professionals' perceptions of AI regulation in a Costa Rican hospital analyze the alignment Latin American regulations with local realities. Methods The research is qualitative, descriptive, cross-sectional, focusing on guidelines laws health at both international national levels. sample includes professionals from private Rica. Two instruments were used: documentary review an online survey. Data analysis was performed using descriptive correlational statistics RStudio (R Foundation for Statistical Computing, Vienna, Austria (https://www.R-project.org/)). Results Eighty participated study. Findings revealed that most exhibited moderate familiarity while underscoring critical need robust governance frameworks navigate regulatory complexities surrounding implementation. Notably, no significant correlation emerged between demographic factors. Limitations this include focus single heterogeneous landscape across countries. Conclusions reveals integration promising but complex, requiring multidimensional approach technical, ethical, social aspects. Healthcare Rica show favorable disposition towards AI, recognizing potential improve healthcare, although they also highlight concerns about privacy, security, ethics.

Language: Английский

---