Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score.

Introduction: Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) models. Originally developed for natural language processing, they have been adapted for multi-modal tasks with vision-language input. One clinically relevant task is scoring the Boston Bowel Preparation Scale (BBPS).

Comparison of the Quality of Discharge Letters Written by Large Language Models and Junior Clinicians: Single-Blinded Study.

Background: Discharge letters are a critical component in the continuity of care between specialists and primary care providers. However, these letters are time-consuming to write, underprioritized in comparison to direct clinical care, and are often tasked to junior doctors. Prior studies assessing the quality of discharge summaries written for inpatient hospital admissions show inadequacies in many domains.

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.

Background: Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.

Main Text: This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management.

Ethical and regulatory challenges of large language models in medicine.

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs.

Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions.

This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global problem exacerbated by the COVID-19 pandemic. We provide a broad discussion on current developments in digital health and artificial intelligence (AI), including large language models (LLMs), as transformative tools in addressing SDOH factors, offering new capabilities for disease surveillance and patient care. Simultaneously, we bring attention to challenges, such as data standardization, infrastructure limitations, digital literacy, and algorithmic bias, that could hinder equitable access to AI benefits.

A Retrospective Cohort Study Evaluating the Safety and Efficacy of Sequential versus Concurrent Intrapleural Instillation of Tissue Plasminogen Activator and DNase for Pleural Infection.

Methods: We conducted a retrospective review of patients with pleural infection requiring intrapleural therapy at two tertiary referral centres.

Results: We included 84 (62.2%) and 51 (37.

The next generation of healthcare ecosystem in the metaverse.

The Metaverse has gained wide attention for being the application interface for the next generation of Internet. The potential of the Metaverse is growing, as Web 3·0 development and adoption continues to advance medicine and healthcare. We define the next generation of interoperable healthcare ecosystem in the Metaverse.

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers.

Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as "consumers", "translators", or "developers". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM).

A translational perspective towards clinical AI fairness.

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite extensive developments, issues of AI fairness in clinical contexts have not been adequately addressed. A fair model is normally expected to perform equally across subgroups defined by sensitive variables (e.

Therapeutic drug monitoring of meropenem and piperacillin-tazobactam in the Singapore critically ill population - A prospective, multi-center, observational study (BLAST 1).

Purpose: To determine percentage of patients with sub-therapeutic beta-lactam exposure in our intensive care units (ICU) and to correlate target attainment with clinical outcomes.

Materials And Methods: Multi-centre, prospective, observational study was conducted in ICUs from three hospitals in Singapore from July 2016 to May 2018. Adult patients (≥21 years) receiving meropenem or piperacillin-tazobactam were included.

An open label randomized trial to assess the efficacy of tranexamic acid in reducing post-operative recurrence of chronic subdural haemorrhage.

Chronic subdural haemorrhage (CSDH) is a common neurosurgical entity with complex pathophysiological pathways. The generally favourable surgical outcome may be affected by its associated risks including recurrence rates. We performed a prospective randomized multi-center clinical trial comparing the addition of tranexamic acid (TXA) to standard neurosurgical procedures for patients with symptomatic CSDH.

Development of stealth liposome coencapsulating doxorubicin and fluoxetine.

Stealth liposomes form an important subset of liposomes, demonstrating prolonged circulation half-life and improved safety in vivo. Caelyx® (liposomal doxorubicin; Merck & Co., Whitehouse Station, New Jersey, USA) is a successful example of the application of stealth liposomes in anticancer treatment.

Quantification of 3-deazaneplanocin A, a novel epigenetic anticancer agent, in rat biosamples by hydrophilic interaction liquid chromatography-tandem mass spectrometric detection.

A sensitive and selective LC-MS/MS based bioanalytical method was developed and validated for the quantification of 3-deazaneplanocin A (DZNep), a novel epigenetic anti-tumor drug candidate, in Sprague-Dawley (SD) rat biosamples (plasma, urine, feces and tissue samples). The method comprises a phenylboronic acid (PBA)-containing solid phase extraction procedure, serving for binding and clean-up of DZNep in rat biosamples spiked with tubercidin (as internal standard). The analytes were separated on an Agilent hydrophilic interaction chromatography (HILIC) column.