Advancing clinical biochemistry: addressing gaps and driving future innovations

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Апрель 8, 2025

Modern healthcare depends fundamentally on clinical biochemistry for disease diagnosis and therapeutic guidance. The discipline encounters operational constraints, including sampling inefficiencies, precision limitations, expansion difficulties. Recent advancements in established technologies, such as mass spectrometry the development of high-throughput screening point-of-care are revolutionizing industry. biosensor technology wearable monitors facilitate continuous health tracking, Artificial Intelligence (AI)/machine learning (ML) applications enhance analytical capabilities, generating predictive insights individualized treatment protocols. However, concerns regarding algorithmic bias, data privacy, lack transparency decision-making (“black box” models), over-reliance automated systems pose significant challenges that must be addressed responsible AI integration. limitations remain—substantial implementation expenses, system incompatibility issues, information security vulnerabilities intersect with ethical considerations fairness protected information. Addressing these demands coordinated efforts between clinicians, scientists, technical specialists. This review discusses current biochemistry, explicitly addressing reference intervals barriers to implementing innovative biomarkers medical settings. discussion evaluates how advanced technologies multidisciplinary collaboration can overcome constraints while identifying research priorities diagnostic accessibility better delivery.

Язык: Английский

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A look at the emerging trends of large language models in ophthalmology

Current Opinion in Ophthalmology, Год журнала: 2024, Номер unknown

Опубликована: Окт. 24, 2024

Purpose of review As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications ophthalmology continue to expand, this seeks update physicians current progress, catalyze further work harness its capabilities enhance healthcare delivery ophthalmology. Recent findings Generative AI have shown promising performance Ophthalmology. Beyond native LLMs question-answering based tasks, there has been increasing employing novel LLM techniques exploring wider use case applications. Summary In review, we first look at existing specific Ophthalmology, followed by an overview commonly used techniques. We finally focus on emerging trends space with angle from

Язык: Английский

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Fostering AI literacy for future librarians

College & Undergraduate Libraries, Год журнала: 2024, Номер unknown, С. 1 - 25

Опубликована: Ноя. 11, 2024

This study assesses the artificial intelligence (AI) literacy and perceptions among Master of Library Science (MLS) students in India. It aims to identify most relevant AI knowledge areas for library science, examine students' attitudes toward AI's potential impact on services, explores importance professional practice. A quantitative survey administered 118 final-year MLS via WhatsApp groups personal networks collected data levels, perceptions, attitudes. The findings reveal that natural language processing (NLP) is considered transformative technology, with significant interest other like recommender systems mining. Students express optimism about enhance services but raise concerns ethical implications job displacement. highlights need increased training integration into LIS programs, emphasizing its future career competitiveness. results provide valuable insights how can be fostered align evolving user needs.

Язык: Английский

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The Use of Machine Learning in Real-World Data: A Systematic Review of Disease Prediction and Management (Preprint)

Опубликована: Ноя. 17, 2024

Machine learning (ML) and big data analytics are revolutionizing healthcare, particularly in disease prediction, management, personalized care. With vast amounts of real-world (RWD) from sources like electronic health records (EHRs), patient registries, wearable devices, ML offers significant potential to improve clinical outcomes. However, quality, transparency, integration challenges remain. This study aims systematically review the use for prediction identifying most common methods, types, designs, evidence (RWE). A systematic followed PRISMA guidelines identify studies that utilized machine methods analyzing management. The focused on extracting related algorithms used, categories, types studies, RWE, such as devices. revealed frequently employed were Random Forest (RF), Logistic Regression (LR), Support Vector (SVM). These applied across various with cardiovascular diseases, cancers, neurological disorders being common. Real-world primarily originated EHRs, a predominant focus predictive modeling hold promise enhancing healthcare through better model interpretability, generalizability must be addressed integrate models fully into practice.

Язык: Английский

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The Use of Machine Learning in Real-World Data: A Systematic Review of Disease Prediction and Management (Preprint)

JMIR Medical Informatics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 17, 2024

Язык: Английский

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