35 research outputs found
Large language models in medicine: the potentials and pitfalls
Large language models (LLMs) have been applied to tasks in healthcare,
ranging from medical exam questions to responding to patient questions. With
increasing institutional partnerships between companies producing LLMs and
healthcare systems, real world clinical application is coming closer to
reality. As these models gain traction, it is essential for healthcare
practitioners to understand what LLMs are, their development, their current and
potential applications, and the associated pitfalls when utilized in medicine.
This review and accompanying tutorial aim to give an overview of these topics
to aid healthcare practitioners in understanding the rapidly changing landscape
of LLMs as applied to medicine
TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
Telehealth is an increasingly critical component of the health care
ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of
telehealth has exposed limitations in the existing infrastructure. In this
paper, we study and highlight photo quality as a major challenge in the
telehealth workflow. We focus on teledermatology, where photo quality is
particularly important; the framework proposed here can be generalized to other
health domains. For telemedicine, dermatologists request that patients submit
images of their lesions for assessment. However, these images are often of
insufficient quality to make a clinical diagnosis since patients do not have
experience taking clinical photos. A clinician has to manually triage poor
quality images and request new images to be submitted, leading to wasted time
for both the clinician and the patient. We propose an automated image
assessment machine learning pipeline, TrueImage, to detect poor quality
dermatology photos and to guide patients in taking better photos. Our
experiments indicate that TrueImage can reject 50% of the sub-par quality
images, while retaining 80% of good quality images patients send in, despite
heterogeneity and limitations in the training data. These promising results
suggest that our solution is feasible and can improve the quality of
teledermatology care.Comment: 12 pages, 5 figures, Preprint of an article published in Pacific
Symposium on Biocomputing \c{opyright} 2020 World Scientific Publishing Co.,
Singapore, http://psb.stanford.edu
Towards Reliable Dermatology Evaluation Benchmarks
Benchmark datasets for digital dermatology unwittingly contain inaccuracies
that reduce trust in model performance estimates. We propose a
resource-efficient data cleaning protocol to identify issues that escaped
previous curation. The protocol leverages an existing algorithmic cleaning
strategy and is followed by a confirmation process terminated by an intuitive
stopping criterion. Based on confirmation by multiple dermatologists, we remove
irrelevant samples and near duplicates and estimate the percentage of label
errors in six dermatology image datasets for model evaluation promoted by the
International Skin Imaging Collaboration. Along with this paper, we publish
revised file lists for each dataset which should be used for model evaluation.
Our work paves the way for more trustworthy performance assessment in digital
dermatology.Comment: Link to the revised file lists:
https://github.com/Digital-Dermatology/SelfClean-Revised-Benchmark
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
Telemedicine utilization was accelerated during the COVID-19 pandemic, and
skin conditions were a common use case. However, the quality of photographs
sent by patients remains a major limitation. To address this issue, we
developed TrueImage 2.0, an artificial intelligence (AI) model for assessing
patient photo quality for telemedicine and providing real-time feedback to
patients for photo quality improvement. TrueImage 2.0 was trained on 1700
telemedicine images annotated by clinicians for photo quality. On a
retrospective dataset of 357 telemedicine images, TrueImage 2.0 effectively
identified poor quality images (Receiver operator curve area under the curve
(ROC-AUC) =0.78) and the reason for poor quality (Blurry ROC-AUC=0.84, Lighting
issues ROC-AUC=0.70). The performance is consistent across age, gender, and
skin tone. Next, we assessed whether patient-TrueImage 2.0 interaction led to
an improvement in submitted photo quality through a prospective clinical pilot
study with 98 patients. TrueImage 2.0 reduced the number of patients with a
poor-quality image by 68.0%.Comment: 24 pages, 7 figure
Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma
Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility
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Pathway analysis of genome-wide data improves warfarin dose prediction
Background: Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations. Results: Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association. Conclusions: Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning
Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm
While artificial intelligence (AI) holds promise for supporting healthcare
providers and improving the accuracy of medical diagnoses, a lack of
transparency in the composition of datasets exposes AI models to the
possibility of unintentional and avoidable mistakes. In particular, public and
private image datasets of dermatological conditions rarely include information
on skin color. As a start towards increasing transparency, AI researchers have
appropriated the use of the Fitzpatrick skin type (FST) from a measure of
patient photosensitivity to a measure for estimating skin tone in algorithmic
audits of computer vision applications including facial recognition and
dermatology diagnosis. In order to understand the variability of estimated FST
annotations on images, we compare several FST annotation methods on a diverse
set of 460 images of skin conditions from both textbooks and online dermatology
atlases. We find the inter-rater reliability between three board-certified
dermatologists is comparable to the inter-rater reliability between the
board-certified dermatologists and two crowdsourcing methods. In contrast, we
find that the Individual Typology Angle converted to FST (ITA-FST) method
produces annotations that are significantly less correlated with the experts'
annotations than the experts' annotations are correlated with each other. These
results demonstrate that algorithms based on ITA-FST are not reliable for
annotating large-scale image datasets, but human-centered, crowd-based
protocols can reliably add skin type transparency to dermatology datasets.
Furthermore, we introduce the concept of dynamic consensus protocols with
tunable parameters including expert review that increase the visibility of
crowdwork and provide guidance for future crowdsourced annotations of large
image datasets
Chapter 7: Pharmacogenomics
<div><p>There is great variation in drug-response phenotypes, and a “one size fits all” paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.</p> </div
Chapter 7: Pharmacogenomics.
There is great variation in drug-response phenotypes, and a "one size fits all" paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics
Drug discovery.
<p>Pharmacogenomics can be used at multiple steps along the drug discovery pipeline to minimize costs, as well as increase throughput and safety. First, association and expression methods (as well as pathway analysis) can be used to identify potential gene targets for a given disease. Cheminformatics can then be used to narrow the number of targets to be tested biochemically, as well as identifying potential polypharmacological factors that could contribute to adverse events. After initial trials (including animal models and Phase I trials), pharmacogenomics can identify variants that may potentially affect dosing and efficacy. This information can then be used in designing a larger Phase III clinical trial, excluding “non-responding” and targeting the drug towards those more likely to respond favorably.</p