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Yuan Liu

Yuan Liu

Yuan is a researcher and engineer in Google Research. She is currently exploring large language models in health. She has led various dermatology related research and development efforts, with a focus on building AI assistant tools to improve global access to accurate information and care for dermatologic conditions, which includes notably DermAssist and Derm features in Google Lens . She also worked on applying computer vision techniques to large-scale user generated content in Google Maps. Prior to joining Google, she obtained her PhD in medical image processing at Vanderbilt University, where she conducted intelligent analysis of radiology images (CT, MRI) to advance image-guided neurosurgical procedures. She has been serving as a co-chair for the ISIC Skin Image Analysis workshop at CVPR/ECCV/MICCAI, and is a guest editor for Medical Image Analysis and IEEE JBHI.
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    Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study
    Terry Spitz
    Malcolm Chelliah
    Heather Cole-Lewis
    Stephanie Farquhar
    Qinghan Xue
    Jenna Lester
    Cían Hughes
    Patricia Strachan
    Fraser Tan
    Peggy Bui
    Craig Mermel
    Lily Peng
    Sunny Virmani
    Ivor Horn
    Cameron Chen
    The Lancet eClinicalMedicine (2024)
    Preview abstract Background Artificial intelligence (AI) has repeatedly been shown to encode historical inequities in healthcare. We aimed to develop a framework to quantitatively assess the performance equity of health AI technologies and to illustrate its utility via a case study. Methods Here, we propose a methodology to assess whether health AI technologies prioritise performance for patient populations experiencing worse outcomes, that is complementary to existing fairness metrics. We developed the Health Equity Assessment of machine Learning performance (HEAL) framework designed to quantitatively assess the performance equity of health AI technologies via a four-step interdisciplinary process to understand and quantify domain-specific criteria, and the resulting HEAL metric. As an illustrative case study (analysis conducted between October 2022 and January 2023), we applied the HEAL framework to a dermatology AI model. A set of 5420 teledermatology cases (store-and-forward cases from patients of 20 years or older, submitted from primary care providers in the USA and skin cancer clinics in Australia), enriched for diversity in age, sex and race/ethnicity, was used to retrospectively evaluate the AI model's HEAL metric, defined as the likelihood that the AI model performs better for subpopulations with worse average health outcomes as compared to others. The likelihood that AI performance was anticorrelated to pre-existing health outcomes was estimated using bootstrap methods as the probability that the negated Spearman's rank correlation coefficient (i.e., “R”) was greater than zero. Positive values of R suggest that subpopulations with poorer health outcomes have better AI model performance. Thus, the HEAL metric, defined as p (R >0), measures how likely the AI technology is to prioritise performance for subpopulations with worse average health outcomes as compared to others (presented as a percentage below). Health outcomes were quantified as disability-adjusted life years (DALYs) when grouping by sex and age, and years of life lost (YLLs) when grouping by race/ethnicity. AI performance was measured as top-3 agreement with the reference diagnosis from a panel of 3 dermatologists per case. Findings Across all dermatologic conditions, the HEAL metric was 80.5% for prioritizing AI performance of racial/ethnic subpopulations based on YLLs, and 92.1% and 0.0% respectively for prioritizing AI performance of sex and age subpopulations based on DALYs. Certain dermatologic conditions were significantly associated with greater AI model performance compared to a reference category of less common conditions. For skin cancer conditions, the HEAL metric was 73.8% for prioritizing AI performance of age subpopulations based on DALYs. Interpretation Analysis using the proposed HEAL framework showed that the dermatology AI model prioritised performance for race/ethnicity, sex (all conditions) and age (cancer conditions) subpopulations with respect to pre-existing health disparities. More work is needed to investigate ways of promoting equitable AI performance across age for non-cancer conditions and to better understand how AI models can contribute towards improving equity in health outcomes. View details
    Differences between Patient and Clinician Submitted Images: Implications for Virtual Care of Skin Conditions
    Rajeev Rikhye
    Grace Eunhae Hong
    Margaret Ann Smith
    Aaron Loh
    Vijaytha Muralidharan
    Doris Wong
    Michelle Phung
    Nicolas Betancourt
    Bradley Fong
    Rachna Sahasrabudhe
    Khoban Nasim
    Alec Eschholz
    Kat Chou
    Peggy Bui
    Justin Ko
    Steven Lin
    Mayo Clinic Proceedings: Digital Health (2024)
    Preview abstract Objective: To understand and highlight the differences in clinical, demographic, and image quality characteristics between patient-taken (PAT) and clinic-taken (CLIN) photographs of skin conditions. Patients and Methods: This retrospective study applied logistic regression to data from 2500 deidentified cases in Stanford Health Care’s eConsult system, from November 2015 to January 2021. Cases with undiagnosable or multiple conditions or cases with both patient and clinician image sources were excluded, leaving 628 PAT cases and 1719 CLIN cases. Demographic characteristic factors, such as age and sex were self-reported, whereas anatomic location, estimated skin type, clinical signs and symptoms, condition duration, and condition frequency were summarized from patient health records. Image quality variables such as blur, lighting issues and whether the image contained skin, hair, or nails were estimated through a deep learning model. Results: Factors that were positively associated with CLIN photographs, post-2020 were as follows: age 60 years or older, darker skin types (eFST V/VI), and presence of skin growths. By contrast, factors that were positively associated with PAT photographs include conditions appearing intermittently, cases with blurry photographs, photographs with substantial nonskin (or nail/hair) regions and cases with more than 3 photographs. Within the PAT cohort, older age was associated with blurry photographs. Conclusion: There are various demographic, clinical, and image quality characteristic differences between PAT and CLIN photographs of skin concerns. The demographic characteristic differences present important considerations for improving digital literacy or access, whereas the image quality differences point to the need for improved patient education and better image capture workflows, particularly among elderly patients. View details
    Preview abstract Machine learning (ML) holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. An important step is to characterize the (un)fairness of ML models—their tendency to perform differently across subgroups of the population—and to understand its underlying mechanisms. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Diagnosing this phenomenon is difficult as sensitive attributes may be causally linked with disease. Using multitask learning, we propose a method to directly test for the presence of shortcut learning in clinical ML systems and demonstrate its application to clinical tasks in radiology and dermatology. Finally, our approach reveals instances when shortcutting is not responsible for unfairness, highlighting the need for a holistic approach to fairness mitigation in medical AI. View details
    Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
    Laura Anne Culp
    Jan Freyberg
    Basil Mustafa
    Sebastien Baur
    Simon Kornblith
    Ting Chen
    Patricia MacWilliams
    Sara Mahdavi
    Megan Zoë Walker
    Aaron Loh
    Cameron Chen
    Scott Mayer McKinney
    Zach William Beaver
    Fiona Keleher Ryan
    Mozziyar Etemadi
    Umesh Telang
    Lily Hao Yi Peng
    Geoffrey Everest Hinton
    Mohammad Norouzi
    Nature Biomedical Engineering (2023)
    Preview abstract Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such ‘out of distribution’ performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for ‘Robust and Efficient Medical Imaging with Self-supervision’), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1–33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging. View details
    Preview abstract Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantly, the success of any mitigation strategy strongly depends on the structure of the shift. Despite this, there has been little discussion of how to empirically assess the structure of a distribution shift that one is encountering in practice. In this work, we adopt a causal framing to motivate conditional independence tests as a key tool for characterizing distribution shifts. Using our approach in two medical applications, we show that this knowledge can help diagnose failures of fairness transfer, including cases where real-world shifts are more complex than is often assumed in the literature. Based on these results, we discuss potential remedies at each step of the machine learning pipeline. View details
    Preview abstract Background: Many dermatologic cases are first evaluated by primary care physicians or nurse practitioners. Objective: This study aimed to evaluate an artificial intelligence (AI)-based tool that assists with interpreting dermatologic conditions. Methods: We developed an AI-based tool and conducted a randomized multi-reader, multi-case study (20 primary care physicians, 20 nurse practitioners, and 1047 retrospective teledermatology cases) to evaluate its utility. Cases were enriched and comprised 120 skin conditions. Readers were recruited to optimize for geographical diversity; the primary care physicians practiced across 12 states (2-32 years of experience, mean 11.3 years), and the nurse practitioners practiced across 9 states (2-34 years of experience, mean 13.1 years). To avoid memory effects from incomplete washout, each case was read once by each clinician either with or without AI assistance, with the assignment randomized. The primary analyses evaluated the top-1 agreement, defined as the agreement rate of the clinicians’ primary diagnosis with the reference diagnoses provided by a panel of dermatologists (per case: 3 dermatologists from a pool of 12, practicing across 8 states, with 5-13 years of experience, mean 7.2 years of experience). We additionally conducted subgroup analyses stratified by cases’ self-reported race and ethnicity and measured the performance spread: the maximum performance subtracted by the minimum across subgroups. Results: The AI’s standalone top-1 agreement was 63%, and AI assistance was significantly associated with higher agreement with reference diagnoses. For primary care physicians, the increase in diagnostic agreement was 10% (P<.001), from 48% to 58%; for nurse practitioners, the increase was 12% (P<.001), from 46% to 58%. When stratified by cases’ self-reported race or ethnicity, the AI’s performance was 59%-62% for Asian, Native Hawaiian, Pacific Islander, other, and Hispanic or Latinx individuals and 67% for both Black or African American and White subgroups. For the clinicians, AI assistance–associated improvements across subgroups were in the range of 8%-12% for primary care physicians and 8%-15% for nurse practitioners. The performance spread across subgroups was 5.3% unassisted vs 6.6% assisted for primary care physicians and 5.2% unassisted vs 6.0% assisted for nurse practitioners. In both unassisted and AI-assisted modalities, and for both primary care physicians and nurse practitioners, the subgroup with the highest performance on average was Black or African American individuals, though the differences with other subgroups were small and had overlapping 95% CIs. Conclusions: AI assistance was associated with significantly improved diagnostic agreement with dermatologists. Across race and ethnicity subgroups, for both primary care physicians and nurse practitioners, the effect of AI assistance remained high at 8%-15%, and the performance spread was similar at 5%-7%. View details
    Preview abstract Importance: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). Objective: To evaluate an artificial intelligence (AI)–based tool that assists with diagnoses of dermatologic conditions. Design, Setting, and Participants: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to April 28, 2020. Data were analyzed from May 26, 2020, to January 27, 2021. Exposures: An AI-based assistive tool for interpreting clinical images and associated medical history. Main Outcomes and Measures: The primary analysis evaluated agreement with reference diagnoses provided by a panel of 3 dermatologists for PCPs and NPs. Secondary analyses included diagnostic accuracy for biopsy-confirmed cases, biopsy and referral rates, review time, and diagnostic confidence. Results: Forty board-certified clinicians, including 20 PCPs (14 women [70.0%]; mean experience, 11.3 [range, 2-32] years) and 20 NPs (18 women [90.0%]; mean experience, 13.1 [range, 2-34] years) reviewed 1048 retrospective cases (672 female [64.2%]; median age, 43 [interquartile range, 30-56] years; 41 920 total reviews) from a teledermatology practice serving 11 sites and provided 0 to 5 differential diagnoses per case (mean [SD], 1.6 [0.7]). The PCPs were located across 12 states, and the NPs practiced in primary care without physician supervision across 9 states. The NPs had a mean of 13.1 (range, 2-34) years of experience and practiced in primary care without physician supervision across 9 states. Artificial intelligence assistance was significantly associated with higher agreement with reference diagnoses. For PCPs, the increase in diagnostic agreement was 10% (95% CI, 8%-11%; P < .001), from 48% to 58%; for NPs, the increase was 12% (95% CI, 10%-14%; P < .001), from 46% to 58%. In secondary analyses, agreement with biopsy-obtained diagnosis categories of maglignant, precancerous, or benign increased by 3% (95% CI, −1% to 7%) for PCPs and by 8% (95% CI, 3%-13%) for NPs. Rates of desire for biopsies decreased by 1% (95% CI, 0-3%) for PCPs and 2% (95% CI, 1%-3%) for NPs; the rate of desire for referrals decreased by 3% (95% CI, 1%-4%) for PCPs and NPs. Diagnostic agreement on cases not indicated for a dermatologist referral increased by 10% (95% CI, 8%-12%) for PCPs and 12% (95% CI, 10%-14%) for NPs, and median review time increased slightly by 5 (95% CI, 0-8) seconds for PCPs and 7 (95% CI, 5-10) seconds for NPs per case. Conclusions and Relevance: Artificial intelligence assistance was associated with improved diagnoses by PCPs and NPs for 1 in every 8 to 10 cases, indicating potential for improving the quality of dermatologic care. View details
    Preview abstract Supervised deep learning models have proven to be highly effective in classification of dermatological conditions. These models rely on the availability of abundant labeled training examples. However, in the real world, many dermatological conditions are individually too infrequent for per-condition classification with supervised learning. Although individually infrequent, these conditions may collectively be common and therefore are clinically significant in aggregate. To avoid models generating erroneous outputs on such examples, there remains a considerable unmet need for deep learning systems that can better detect such infrequent conditions. These infrequent `outlier' conditions are seen very rarely (or not at all) during training. In this paper, we frame this task as an out-of-distribution (OOD) detection problem. We set up a benchmark ensuring that outlier conditions are disjoint between model train, validation, and test sets. Unlike most traditional OOD benchmarks which detect dataset distribution shift, we aim at detecting semantic differences, often referred to as near-OOD detection which is a more difficult task. We propose a novel hierarchical outlier detection (HOD) approach, which assigns multiple abstention classes for each training outlier class and jointly performs a coarse classification of inliers \vs{} outliers, along with fine-grained classification of the individual classes. We demonstrate that the proposed HOD outperforms existing techniques for outlier exposure based OOD detection. We also use different state-of-the-art representation learning approaches (BiT-JFT, SimCLR, MICLe) to improve OOD performance and demonstrate the effectiveness of HOD loss for them. Further, we explore different ensembling strategies for OOD detection and propose a diverse ensemble selection process for the best result. We also performed a subgroup analysis over conditions of varying risk levels and different skin types to investigate how OOD performance changes over each subgroup and demonstrated the gains of our framework in comparison to baselines. Furthermore, we go beyond traditional performance metrics and introduce a cost metric to approximate downstream clinical impact. We used this cost metric to compare the proposed method against the baseline, thereby making a stronger case for its effectiveness in real-world deployment scenarios. View details
    Supervised Transfer Learning at Scale for Medical Imaging
    Aaron Loh
    Basil Mustafa
    Jan Freyberg
    Patricia MacWilliams
    Megan Wilson
    Scott Mayer McKinney
    Peggy Bui
    Umesh Telang
    ArXiV (2021)
    Preview abstract Transfer learning is a standard building block of successful medical imaging models, yet previous efforts suggest that at limited scale of pre-training data and model capacity, benefits of transfer learning to medical imaging are insubstantial. In this work, we explore whether scaling up pre-training can help improve transfer to medical tasks. In particular, we show that when using the Big Transfer recipe to further scale up pre-training, we can indeed considerably improve transfer performance across three popular yet diverse medical imaging tasks - interpretation of chest radiographs, breast cancer detection from mammograms and skin condition detection from smartphone images. Despite pre-training on unrelated source domains, we show that scaling up the model capacity and pre-training data yields performance improvements regardless of how much downstream medical data is available. In particular, we show suprisingly large improvements to zero-shot generalisation under distribution shift. Probing and quantifying other aspects of model performance relevant to medical imaging and healthcare, we demonstrate that these gains do not come at the expense of model calibration or fairness. View details
    Machine learning for clinical operations improvement via case triaging
    Susan Jen Huang
    Kimberly Kanada
    Lily Hao Yi Peng
    Peggy Bui
    Skin Health and Disease (2021)
    Preview abstract In recent years, an increasing number of machine learning (ML) models have been developed for interpreting images of skin conditions and for risk stratification. Beyond accurate image interpretation, one potential application of these interpretations may be triaging systems to help direct care to the right care provider at the right time. This is a critical need because dermatologist appointment wait times exceed a month in many regions, a trend that can potentially be alleviated by rapidly stratifying patients to clinicians with the appropriate level of training (e.g., board-certified dermatologist, advanced practice provider under dermatologist supervision, non-dermatologist) and the appropriate urgency. To help understand ML's potential for this triaging, we analysed a previously-described deep learning system (DLS) that provides a differential diagnosis of teledermatology cases and that improved the diagnostic accuracy of primary care physicians and nurse practitioners in a randomized study. We reordered the cases within each ‘review batch’ of 500 based on the urgency category of the DLS-predicted skin condition (which is an automated process requiring no human intervention). On average, this caused the review order of urgent cases to be prioritised substantially sooner than that of less urgent cases, with the average rank of ‘immediate intervention cases’ being about 100 (vs. 253 without reordering, p < 0.001), and that of ‘no need to see a doctor’ cases being close to 400 (vs. 252 without reordering, p < 0.001). Our approach has the potential to accelerate triaging and reduce the burden on the limited dermatology workforce to focus on patient management. View details
    Agreement Between Saliency Maps and Human-Labeled Regions of Interest: Applications to Skin Disease Classification
    Singh Nalini
    Kang Lee
    Susan Huang
    Aaron Loh
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)
    Preview abstract We propose to systematically identify potentially problematic patterns in skin disease classification models via quantitative analysis of agreement between saliency maps and human-labeled regions of interest. We further compute summary statistics describing patterns in this agreement for various stratifications of input examples. Through this analysis, we discover candidate spurious associations learned by the classifier and suggest next steps to handle such associations. Our approach can be used as a debugging tool to systematically spot difficult examples and error categories. Insights from this analysis could guide targeted data collection and improve model generalizability. View details
    A deep learning system for differential diagnosis of skin diseases
    Clara Eng
    David Way
    Kang Lee
    Peggy Bui
    Kimberly Kanada
    Guilherme de Oliveira Marinho
    Jess Gallegos
    Sara Gabriele
    Vishakha Gupta
    Nalini Singh
    Lily Peng
    Dennis Ai
    Susan Huang
    Carter Dunn
    Nature Medicine (2020)
    Preview abstract Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions. View details
    Preview abstract Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards majority classes and ignore the importance of the rest. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which rarely occur in the wild. Buoyed by recent advances, we explore meta-learning based few-shot learning approaches in skin condition recognition problem and propose an evaluation setup to fairly assess the real-world impact of such approaches. When compared to conventional class imbalance techniques, we find that the state-of-the-art few-shot learning methods are not as performant, but combining the two approaches using a novel ensemble leads to improvement in all-way classification, especially the rare classes. We conclude that the ensemble can be useful to address the class imbalance problem, yet progress here can further be accelerated by the use of real-world evaluation setups for benchmarking new methods. View details
    Artificial Intelligence and Malignant Melanoma
    Artificial Intelligence Approach in Melanoma, Springer (2019)
    Preview abstract Since its inception in the mid-twentieth century, the field of artificial intelligence (AI) has undergone numerous transformations and retreats. Using large datasets, powerful computers, and modern computational methods, the subset of AI known as machine learning can identify complex patterns in real-world data, yielding observations, associations, and predictions that can match or exceed human capabilities. After decades of promise, the field stands poised to influence a broad range of human endeavors, from the most complex strategic games to autonomous vehicle navigation, financial engineering, and health care. Therefore, the purpose of this chapter is to provide an introduction to AI approaches and medical applications while elaborating on the role of AI in malignant melanoma detection and diagnosis from a healthcare provider and consumer perspective. It is critical that we continue to balance the opportunity and threat of AI in malignant melanoma, as this technology becomes more robust to maximize an effective implementation. View details
    Preview abstract Despite the recent success in applying supervised deep learning to medical imaging tasks, the problem of obtaining large, diverse and expert-annotated datasets required for development of high performant models remains particularly challenging. In this work, we explore the possibility of using Generative Adverserial Networks (GAN) to synthesize natural images with skin pathology. We propose DermGAN, an adaptation of the popular Pix2Pix architecture, to create synthetic images for a pre-specified skin condition, with varying size and location, and the underlying skin color. In a human turing test, we show that the synthetic images are not only visually similar to real images, but also embody the respective skin condition in dermatologists' eyes. Furthermore, when using synthetic images as a data augmentation technique for training a skin condition classifier, the model is non-inferior to the baseline model while demonstrating improved performance for rare conditions. View details
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