Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care

Journal of the American Medical Informatics Association, Journal Year: 2024, Volume and Issue: 31(6), P. 1436 - 1440

Published: Jan. 25, 2024

Abstract Purpose This article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating burden on clinicians caused by electronic medical records. Potential LLMs offer opportunities clinical documentation, prior authorization, patient education, and access care. They can personalize scheduling, improve documentation accuracy, streamline insurance increase engagement, address barriers healthcare access. Caution However, integrating requires careful attention security privacy concerns, protecting data, complying with regulations like Health Insurance Portability Accountability Act (HIPAA). It is crucial acknowledge that should supplement, not replace, human connection care provided professionals. Conclusion By prudently utilizing alongside expertise, organizations outcomes. Implementation be approached caution consideration ensure safe effective use setting.

Language: Английский

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Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Diagnostic and Interventional Radiology, Journal Year: 2024, Volume and Issue: unknown

Published: July 2, 2024

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into practice may present a double-edged sword due to bias (i.e., systematic errors).AI algorithms have the potential mitigate cognitive biases in human interpretation, but extensive research has highlighted tendency of AI systems internalize within model.This fact, whether intentional or not, ultimately lead unintentional consequences clinical setting, potentially compromising patient outcomes.This concern is particularly important imaging, where been more progressively and widely embraced than any other field.A comprehensive understanding at each stage pipeline therefore essential contribute developing solutions that are not only less biased also applicable.This international collaborative review effort aims increase awareness imaging community about importance proactively identifying addressing prevent its negative from being realized later.The authors began with fundamentals by explaining different definitions delineating various sources.Strategies detecting were then outlined, followed techniques avoidance mitigation.Moreover, ethical dimensions, challenges encountered, prospects discussed.

Language: Английский

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From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Diagnostics, Journal Year: 2024, Volume and Issue: 14(2), P. 174 - 174

Published: Jan. 12, 2024

Pancreatic cancer is a highly aggressive and difficult-to-detect with poor prognosis. Late diagnosis common due to lack of early symptoms, specific markers, the challenging location pancreas. Imaging technologies have improved diagnosis, but there still room for improvement in standardizing guidelines. Biopsies histopathological analysis are tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving treatment, patient care. AI algorithms can analyze medical images precision, aiding disease detection. also plays role personalized medicine analyzing data tailor treatment plans. It streamlines administrative tasks, such as coding documentation, provides assistance through chatbots. However, challenges include privacy, security, ethical considerations. This review article focuses on potential transforming pancreatic care, offering diagnostics, treatments, operational efficiency, leading better outcomes.

Language: Английский

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AI Applications for Thoracic Imaging: Considerations for Best Practice

Radiology, Journal Year: 2025, Volume and Issue: 314(2)

Published: Feb. 1, 2025

This article describes the status and potential expansion of artificial intelligence in thoracic imaging, including practical issues for its clinical implementation into daily practice as well challenges opportunities.

Language: Английский

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Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(6), P. e0299623 - e0299623

Published: June 24, 2024

Background In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce need for human annotation and advance disease classification. Among established techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) has demonstrated efficacy in improving across various modalities, such as X-rays CT. However, there remains a demand improved contrast enhancement methods considering heterogeneity datasets contrasts different anatomic structures. Method This study proposes novel technique, ps-KDE, to investigate its impact on deep learning segment major organs posterior-anterior chest X-rays. Ps-KDE augments image by substituting pixel values based their normalized frequency all images. We evaluate our approach U-Net architecture ResNet34 backbone pre-trained ImageNet. Five separate models are trained heart, left lung, right clavicle, clavicle. Results The model lung using ps-KDE achieved Dice score 0.780 (SD = 0.13), while that CLAHE 0.717 0.19), p <0.01. also appears be more robust CLAHE-based misclassified lungs select test images model. algorithm performing is available at https://github.com/wyc79/ps-KDE . Discussion Our results suggest offers advantages over current when segmenting certain regions. could beneficial subsequent analyses classification risk stratification.

Language: Английский

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Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology

Insights into Imaging, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 14, 2024

Various healthcare domains have witnessed successful preliminary implementation of artificial intelligence (AI) solutions, including radiology, though limited generalizability hinders their widespread adoption. Currently, most research groups and industry access to the data needed for external validation studies. The creation accessibility benchmark datasets validate such solutions represents a critical step towards generalizability, which an array aspects ranging from preprocessing regulatory issues biostatistical principles come into play. In this article, authors provide recommendations in explain current limitations realm, explore potential new approaches. CLINICAL RELEVANCE STATEMENT: Benchmark datasets, facilitating AI software performance can contribute adoption clinical practice. KEY POINTS: are essential performance. Factors like image quality representativeness cases should be considered. help by increasing trustworthiness robustness AI.

Language: Английский

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The paradigm of digital health: AI applications and transformative trends

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 15, 2025

Language: Английский

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Clinical Applications of Artificial Intelligence in Neurology Practice

CONTINUUM Lifelong Learning in Neurology, Journal Year: 2025, Volume and Issue: 31(2), P. 583 - 600

Published: April 1, 2025

ABSTRACT As artificial intelligence (AI) tools become increasingly mainstream, they can potentially transform neurology clinical practice by improving patient care and reducing clinician workload. However, with these promises also come perils, neurologists must understand AI as it becomes integrated into health care. This article presents a brief background on explores some of the potential applications in focus diagnostic testing, documentation, workflows highlighting opportunities to address long-standing human biases challenges mitigation strategies.

Language: Английский

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Advancing pancreatic cancer management: the role of artificial intelligence in diagnosis and therapy

Beni-Suef University Journal of Basic and Applied Sciences, Journal Year: 2025, Volume and Issue: 14(1)

Published: April 7, 2025

Abstract Background Pancreatic cancer is the deadliest form of with a low survival rate due to its late diagnosis. Hence, early detection and swift intervention are very crucial for management. However, current diagnostic markers lack sufficient precision, effectiveness treatment options remains imprecise, emphasizing need more advanced approaches. Main body Artificial intelligence (AI) technology enables rapid high-risk groups pancreatic using various techniques such as medical imaging, pathological examination, biomarkers, other methods, facilitating cancer. Simultaneously, AI algorithms may also be used forecast duration survival, likelihood recurrence, metastasis, response treatment, all which can impact prognosis. Moreover, applied in handling cases oncology departments, particular, creating computer-assisted systems. Conclusion The end-to-end application management calls multidisciplinary collaboration among doctors, laboratory scientists, data analysts, engineers. Despite limitations, powerful computational capabilities will soon combating health conditions.

Language: Английский

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Large Language Models for Global Health Clinics: Opportunities and Challenges

Journal of the American College of Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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