Topolizer

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The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today...

The annual cost of hospital care services in the US has risen to over $1 trillion despite relatively worse health outcomes compared to similar nations. These trends accentuate a growing need for innovative care delivery models that reduce costs and improve outcomes. HaH-a program that provides patients acute-level hospital care at home-has made significant progress over the past two decades. Technological advancements in remote patient monitoring, wearable sensors, health information technology infrastructure, and multimodal health data processing have contributed to its rise across hospitals...

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Long Covid provides an opportunity to understand how acute infections cause chronic disease.

The 12-lead electrocardiogram (ECG) is an integral component to the diagnosis of a multitude of cardiovascular conditions. It is performed using a complex set of skin surface electrodes, limiting its use outside traditional clinical settings. We developed an artificial intelligence algorithm, trained over 600,000 clinically acquired ECGs, to explore whether fewer leads as input are sufficient to reconstruct a full 12-lead ECG. Two limb leads (I and II) and one precordial lead (V3) were required to generate a reconstructed synthetic 12-lead ECG highly correlated with the original ECG...

The medical community does not broadcast the problem, but there are many studies that have reinforced a serious issue with diagnostic errors. A recent study concluded: "We estimate that nearly 800,000 Americans die or are permanently disabled by diagnostic errors each year." Diagnostic errors are inaccurate assessments of a patient's root cause of illness, such as missing a heart attack or infection or assigning the wrong diagnosis of pneumonia when the correct one is pulmonary embolism. Despite ever-increasing use of medical imaging and laboratory tests intended to promote diagnostic accuracy, there is nothing to suggest improvement since the report by the National Academies of Sciences, Engineering and Medicine in 2015, which provided a conservative estimate that 5% of adults experience a diagnostic error each year, and that most people will experience at least one in their lifetime...

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PURPOSE: In Israel, a public committee advises which new medications should be reimbursed subject to an annual budget allocation. The committee considers clinical trial outcomes, professional societies' preferences, projected budget impacts, and other social and ethical aspects. The Israeli oncologists' society places a strong emphasis on prioritizing adjuvant therapies because of their potential to advance cure. In 2023, several novel adjuvant therapies were suggested for national funding...

ObjectiveWhen selecting inhaled therapies, it is important to consider both the active molecules and the device. Extrafine formulation beclomethasone dipropionate plus formoterol fumarate (BDP/FF) has been available for some years delivered via pressurized metered-dose inhaler (pMDI). More recently, a breath-activated, multi-dose dry-powder inhaler (DPI), the NEXThaler, has been approved. The current study aimed to demonstrate the non-inferiority of BDP/FF delivered via the DPI vs. via the pMDI, in Chinese adults with asthma...

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INTRODUCTION: Self-regulated learning is a cyclical process of forethought, performance, and self-reflection that has been used as an assessment tool in medical education. No prior studies have evaluated SRL processes for answering multiple-choice questions (MCQs) and most evaluated one or two iterations of a non-MCQ task. SRL assessment during MCQs may elucidate reasons why learners are successful or not on these questions that are encountered repeatedly during medical education. METHODS: Internal medicine clerkship students at three institutions participated in a SRL microanalytic protocol that targeted strategic planning, metacognitive monitoring, causal attributions, and adaptive inferences across seven MCQs...

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Background Clinicians consider both imaging and nonimaging data when diagnosing diseases; however, current machine learning approaches primarily consider data from a single modality. Purpose To develop a neural network architecture capable of integrating multimodal patient data and compare its performance to models incorporating a single modality for diagnosing up to 25 pathologic conditions. Materials and Methods In this retrospective study, imaging and nonimaging patient data were extracted from the Medical Information Mart for Intensive Care (MIMIC) database and an internal database comprised of chest radiographs and clinical parameters inpatients in the intensive care unit (ICU) (January 2008 to December 2020)...

The adoption of digital technologies in healthcare, accelerated by the COVID-19 pandemic, requires a well-prepared workforce capable of implementing those technologies. Here, we examine the role and impact of digital fellowships in facilitating digital transformation in healthcare systems. Digital fellowships are structured educational programmes designed to equip healthcare professionals with advanced digital skills. Focusing on UK-based initiatives like the Topol Digital Fellowship and the Fellowship in Clinical AI, we explore their efforts to prepare healthcare leaders for digital and AI adoption...

Machines don't have eyes, but you wouldn't know that if you followed the progression of deep learning models for accurate interpretation of medical images, such as x-rays, computed tomography (CT) and magnetic resonance imaging (MRI) scans, pathology slides, and retinal photos. Over the past several years, there has been a torrent of studies that have consistently demonstrated how powerful "machine eyes" can be, not only compared with medical experts but also for detecting features in medical images that are not readily discernable by humans...

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1 . However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2 . Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications...

BACKGROUND AND OBJECTIVES: Cadaveric studies have shown disease-related neurodegeneration and other morphological abnormalities in the retina of individuals with Parkinson disease (PD); however, it remains unclear whether this can be reliably detected with in vivo imaging. We investigated inner retinal anatomy, measured using optical coherence tomography (OCT), in prevalent PD and subsequently assessed the association of these markers with the development of PD using a prospective research cohort...

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The rapid advancements in artificial intelligence (AI) have led to the development of sophisticated large language models (LLMs) such as GPT-4 and Bard. The potential implementation of LLMs in healthcare settings has already garnered considerable attention because of their diverse applications that include facilitating clinical documentation, obtaining insurance pre-authorization, summarizing research papers, or working as a chatbot to answer questions for patients about their specific data and concerns. While offering transformative potential, LLMs warrant a very cautious approach since these models are trained differently from AI-based medical technologies that are regulated already, especially within the critical context of caring for patients...