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Background: Health datasets from clinical sources do not reflect the breadth and diversity of disease in the real world, impacting research, medical education, and artificial intelligence (AI) tool development.
“CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison”, 2019
Jeremy Irvin et al · 1901
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F Rampen, B Fleuren, T de Boo and W Lemmens · 1988
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“Increasing utilization of dermatologists by managed care: an analysis of the National Ambulatory Medical Care Survey, 1990-1994”
S Feldman et al · 1997
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“A retrospective biopsy study of the clinical diagnostic accuracy of common skin diseases by different specialties compared with dermatology”
Klaus Sellheyer and Wilma Bergfeld · 2005
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“The first images of atopic dermatitis: an attempt at retrospective diagnosis in dermatology”
Daniel Wallach, Joël Coste, Gérard Tilles and Alain Taı̈eb · 2005
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“Who searches the internet for health information?”
M Bundorf, Todd Wagner, Sara Singer and Laurence Baker · 2006
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“Successful participant recruitment strategies for an online smokeless tobacco cessation program”
Judith Gordon et al · 2006
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“Health information seeking on the Internet: a double divide? Results from a representative survey in the Paris metropolitan area, France, 2005-2006”
Emilie Renahy, Isabelle Parizot and Pierre Chauvin · 2008
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“Detecting influenza epidemics using search engine query data”
Jeremy Ginsberg et al · 2009
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“The Social Life of Health Information, 2011” Accessed: 2023-11-20
Susannah Fox · 2011
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“PH2 - a dermoscopic image database for research and benchmarking”
Teresa Mendonca et al · 2013
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“A study of internet searches for medical information in dermatology patients: The patient–physician relationship”
J Orgaz-Molina et al · 2015
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“Conducting a fully mobile and randomised clinical trial for depression: access, engagement and expense”
Joaquin Anguera et al · 2016
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“MIMIC-III, a freely accessible critical care database”
Alistair Johnson et al · 2016
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“Impact of store-and-forward (SAF) teledermatology on outpatient dermatologic care: A prospective study in an underserved urban primary care setting”
Caroline Nelson et al · 2016
Earlier work this paper cites.
“Screening for Pancreatic Adenocarcinoma Using Signals From Web Search Logs: Feasibility Study and Results”
John Paparrizos, Ryen White and Eric Horvitz · 2016
Earlier work this paper cites.
“Providing dermatological care in resource-limited settings: barriers and potential solutions”
A Chang, S Kiprono and T Maurer · 2017
Earlier work this paper cites.
“What Predicts Online Health Information-Seeking Behavior Among Egyptian Adults? A Cross-Sectional Study”
Mayada Ghweeba et al · 2017
Earlier work this paper cites.
“Evaluation of the Feasibility of Screening Patients for Early Signs of Lung Carcinoma in Web Search Logs”
Ryen White and Eric Horvitz · 2017
Cited alongside, same era.
“Using Facebook for Large-Scale Online Randomized Clinical Trial Recruitment: Effective Advertising Strategies”
Laura Akers and Judith Gordon · 2018
Cited alongside, same era.
“Machine-learned epidemiology: real-time detection of foodborne illness at scale”
Adam Sadilek et al · 2018
Cited alongside, same era.
“OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis: First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings”
Danail Stoyanov et al · 2018
Cited alongside, same era.
“CheXclusion: Fairness gaps in deep chest X-ray classifiers”
Laleh Seyyed-Kalantari et al · 2021
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“AI-based localization and classification of skin disease with erythema”
Ha Son et al · 2021
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“Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review”
Leo Celi et al · 2022
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“Disparities in dermatology AI performance on a diverse, curated clinical image set”
Roxana Daneshjou et al · 2022
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Matthew Groh et al · 2022
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“The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions”
Philipp Tschandl, Cliff Rosendahl and Harald Kittler · 2018
Cited alongside, same era.
“The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland”
Joana Barros et al · 2019
Cited alongside, same era.
“Light Field Image Dataset of Skin Lesions”
Sergio de Faria et al · 2019
Cited alongside, same era.
“Evaluation of the Number-Needed-to-Biopsy Metric for the Diagnosis of Cutaneous Melanoma: A Systematic Review and Meta-analysis”
Kelly Nelson et al · 2019
Cited alongside, same era.
“Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders”
Seung Han et al · 2020
Cited alongside, same era.
“A deep learning system for differential diagnosis of skin diseases”
Yuan Liu et al · 2020
Cited alongside, same era.
“PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones”
Andre Pacheco et al · 2020
Cited alongside, same era.
“Using Google Ads to recruit and retain a cohort considering abortion in the United States”
Ushma Upadhyay, Iris Jovel, Kevin McCuaig and Alice Cartwright · 2020
Cited alongside, same era.
Matthew Groh et al · 2022
Later among the works it cites.
“Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection-A scoping review”
Lisa Guo et al · 2022
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“How we detect, remove and report child sexual abuse material” Accessed: 2023-11-18
Susan Jasper · 2022
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“Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare”
Giona Kleinberg, Michael Diaz, Sai Batchu and Brandon Lucke-Wold · 2022
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“Survey of Physician Appointment Wait Times and Medicare and Medicaid Acceptance Rates” Accessed: 2023-11-18,
2022
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“Characteristics of publicly available skin cancer image datasets: a systematic review”
David Wen et al · 2022
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“Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection”
Debarpan Bhattacharya et al · 2023
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“Know Your Data” Accessed: 2023-11-20,
2023
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“The Monk Skin Tone Scale”, 2023
Ellis Monk · 2023
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“Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality”
Kailas Vodrahalli et al · 2023
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“Health Information Technology Use Among Adults: United States, July-December 2022”,
Xun Wang and Robin Cohen · 2023
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“The burden of skin and subcutaneous diseases: findings from the global burden of disease study 2019”
Aobuliaximu Yakupu et al · 2023
Later among the works it cites.