Dataset Information

Disparities in dermatology AI performance on a diverse, curated clinical image set.

ABSTRACT: An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.

SUBMITTER: Daneshjou R

PROVIDER: S-EPMC9374341 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Disparities in dermatology AI performance on a diverse, curated clinical image set.

Daneshjou Roxana R Vodrahalli Kailas K Novoa Roberto A RA Jenkins Melissa M Liang Weixin W Rotemberg Veronica V Ko Justin J Swetter Susan M SM Bailey Elizabeth E EE Gevaert Olivier O Mukherjee Pritam P Phung Michelle M Yekrang Kiana K Fong Bradley B Sahasrabudhe Rachna R Allerup Johan A C JAC Okata-Karigane Utako U Zou James J Chiou Albert S AS

Science advances 20220812 32

An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models e ...[more]

PMID: 35960806

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Project description:ObjectivesLarge language models (LLMs) have shown potential in radiology, but their ability to aid radiologists in interpreting imaging studies remains unexplored. We investigated the effects of a state-of-the-art LLM (GPT-4) on the radiologists' diagnostic workflow.Materials and methodsIn this retrospective study, six radiologists of different experience levels read 40 selected radiographic [n = 10], CT [n = 10], MRI [n = 10], and angiographic [n = 10] studies unassisted (session one) and assisted by GPT-4 (session two). Each imaging study was presented with demographic data, the chief complaint, and associated symptoms, and diagnoses were registered using an online survey tool. The impact of Artificial Intelligence (AI) on diagnostic accuracy, confidence, user experience, input prompts, and generated responses was assessed. False information was registered. Linear mixed-effect models were used to quantify the factors (fixed: experience, modality, AI assistance; random: radiologist) influencing diagnostic accuracy and confidence.ResultsWhen assessing if the correct diagnosis was among the top-3 differential diagnoses, diagnostic accuracy improved slightly from 181/240 (75.4%, unassisted) to 188/240 (78.3%, AI-assisted). Similar improvements were found when only the top differential diagnosis was considered. AI assistance was used in 77.5% of the readings. Three hundred nine prompts were generated, primarily involving differential diagnoses (59.1%) and imaging features of specific conditions (27.5%). Diagnostic confidence was significantly higher when readings were AI-assisted (p > 0.001). Twenty-three responses (7.4%) were classified as hallucinations, while two (0.6%) were misinterpretations.ConclusionIntegrating GPT-4 in the diagnostic process improved diagnostic accuracy slightly and diagnostic confidence significantly. Potentially harmful hallucinations and misinterpretations call for caution and highlight the need for further safeguarding measures.Clinical relevance statementUsing GPT-4 as a virtual assistant when reading images made six radiologists of different experience levels feel more confident and provide more accurate diagnoses; yet, GPT-4 gave factually incorrect and potentially harmful information in 7.4% of its responses.

Project description:Background and objectiveMedical image segmentation is a vital aspect of medical image processing, allowing healthcare professionals to conduct precise and comprehensive lesion analyses. Traditional segmentation methods are often labor intensive and influenced by the subjectivity of individual physicians. The advent of artificial intelligence (AI) has transformed this field by reducing the workload of physicians, and improving the accuracy and efficiency of disease diagnosis. However, conventional AI techniques are not without challenges. Issues such as inexplicability, uncontrollable decision-making processes, and unpredictability can lead to confusion and uncertainty in clinical decision-making. This review explores the evolution of AI in medical image segmentation, focusing on the development and impact of explainable AI (XAI) and trustworthy AI (TAI).MethodsThis review synthesizes existing literature on traditional segmentation methods, AI-based approaches, and the transition from conventional AI to XAI and TAI. The review highlights the key principles and advancements in XAI that aim to address the shortcomings of conventional AI by enhancing transparency and interpretability. It further examines how TAI builds on XAI to improve the reliability, safety, and accountability of AI systems in medical image segmentation.Key content and findingsXAI has emerged as a solution to the limitations of conventional AI by providing greater transparency and interpretability, allowing healthcare professionals to better understand and trust AI-driven decisions. However, XAI itself faces challenges, including those related to safety, robustness, and value alignment. TAI has been developed to overcome these challenges, offering a more reliable framework for AI applications in medical image segmentation. By integrating the principles of XAI with enhanced safety and dependability, TAI addresses the critical need for TAI systems in clinical settings.ConclusionsTAI presents a promising future for medical image segmentation, combining the benefits of AI with improved reliability and safety. Thus, TAI is a more viable and dependable option for healthcare applications, and could ultimately lead to better clinical outcomes for patients, and advance the field of medical image processing.

Dataset Information

Disparities in dermatology AI performance on a diverse, curated clinical image set.

Publications

Disparities in dermatology AI performance on a diverse, curated clinical image set.

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