Publications by authors named "Zhuo R Cai"

The integration of large language models (LLMs) into clinical diagnostics has the potential to transform doctor-patient interactions. However, the readiness of these models for real-world clinical application remains inadequately tested. This paper introduces the Conversational Reasoning Assessment Framework for Testing in Medicine (CRAFT-MD) approach for evaluating clinical LLMs.

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Informal caregivers of people with Alzheimer's disease and related dementias (ADRD) are at risk of poor mental health. This study aimed to investigate the feasibility and validity of studying caregivers' mental stressors using online caregiving forum data (March 2018-February 2022) and natural language processing and machine learning (NLP/ML). NLP/ML topic modeling generated eight prominent topics, which we compared with qualitatively defined themes and the existing caregiving framework to assess validity.

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Background: The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence.

Objective: We aim to comprehensively assess telehealth use and its associated factors in the United States.

Methods: This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022.

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Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation.

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The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis.

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The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers.

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This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption.

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We sought to investigate the feasibility of longitudinal monitoring of disease activity from home in people with hidradenitis suppurativa (HS). Over 6 months, our novel digital tool collected 421 photos of HS-affected skin from 27 participants and captured trends in pain and quality of life scores. We found that participants with mild disease were more likely to share their progress than those with more severe disease, which is favourable as it may suggest a role for remote monitoring in tracking disease progression.

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We developed a digital tool for home-based monitoring of skin disease, our digital tool. In the current observational pilot study, we found that DORA is feasible to use in practice, as it has a high patient compliance, retention and satisfaction. Clinicans rated the photos generally good quality or perfect quality.

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Article Synopsis
  • The study investigates the risk of developing subsequent primary cancers in patients diagnosed with cutaneous Merkel cell carcinoma (MCC), highlighting that this risk has not been thoroughly established before.
  • Analyzing data from 17 registries from 2000 to 2019, researchers found that out of 6,146 patients diagnosed with MCC, 725 (11.8%) went on to develop other primary cancers.
  • The results showed an overall increased risk of 28% for subsequent cancers, particularly for cutaneous melanoma, papillary thyroid carcinoma, and non-Hodgkin lymphoma, indicating that patients with MCC should be monitored closely for these additional health risks.
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Article Synopsis
  • Indoor tanning beds lead to over 450,000 new skin cancer cases each year, with a 10.4% global prevalence among users, prompting the need for effective public health messaging on platforms like Instagram.* -
  • A public health campaign on Instagram targeted young women and men at high risk for indoor tanning, featuring 34 ads and reaching over 1 million individuals, with significant engagement on the health information landing page.* -
  • Despite high ad exposure and website visits, the campaign did not result in measurable changes in foot traffic to tanning salons, indicating a need for further research to assess effectiveness.*
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Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET (edical ccept rriever), which learns how to connect medical images with text and generates dense concept annotations to enable tasks in AI transparency from model auditing to model interpretation.

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Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices. Here, we present a general model auditing framework that combines insights from medical experts with a highly expressive form of explainable AI that leverages generative models, to understand the reasoning processes of AI devices. We then apply this framework to generate the first thorough, medically interpretable picture of the reasoning processes of machine-learning-based medical image AI.

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RHOA-related neuroectodermal syndrome is characterised by linear skin hypopigmentation along Blaschko's lines associated with alopecia, leukoencephalopathy, facial and limb hypoplasia, and ocular, dental, and acral anomalies. Herein, we report a patient with patterned cutaneous hypopigmentation with a similar phenotype due to a novel postzygotic RHOA variant (c.210G>T; p.

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Introduction: Stevens-Johnson syndrome and toxic epidermal necrolysis are severe cutaneous drug eruptions characterized by epidermal detachment. Pembrolizumab is a monoclonal antibody that binds to the programmed death-1 receptor, and it has been associated with numerous cutaneous adverse side-effects, including Stevens-Johnson syndrome.

Case Report: We describe a 63-year-old male with metastatic lung adenocarcinoma who developed a rapidly progressing maculopapular rash three days after a first dose of pembrolizumab.

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