Author Interviews, JAMA, Pediatrics, Smoking, Tobacco / 23.10.2019
Kids Who Starting Smoking with Flavored Tobacco More Likely to Keep Smoking
MedicalResearch.com Interview with:
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Dr. Villanti[/caption]
Andrea Villanti, PhD, MPH
Associate Professor
Department of Psychiatry
Vermont Center on Behavior and Health
University of Vermont
MedicalResearch.com: What is the background for this study?
Response: Our earlier work documented a significant association between first use of a flavored tobacco product and current tobacco use (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5522636/) in a cross-sectional sample. The goal of this study was to examine whether there was a prospective relationship between first use of a flavored tobacco product and subsequent use of that product in longitudinal data..
Dr. Villanti[/caption]
Andrea Villanti, PhD, MPH
Associate Professor
Department of Psychiatry
Vermont Center on Behavior and Health
University of Vermont
MedicalResearch.com: What is the background for this study?
Response: Our earlier work documented a significant association between first use of a flavored tobacco product and current tobacco use (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5522636/) in a cross-sectional sample. The goal of this study was to examine whether there was a prospective relationship between first use of a flavored tobacco product and subsequent use of that product in longitudinal data..
Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.

Dr. Hongying (Daisy) Dai[/caption]
Hongying (Daisy) Dai, PhD
Associate Professor
Department of Biostatistics | College of Public Health
University of Nebraska Medical Center
MedicalResearch.com: What is the background for this study?
Response: Although marijuana is still classified as a Schedule I drug at the Federal level, as of June 2019, 33 states and the District of Columbia have legalized one or more forms of marijuana; 11 states and the District of Columbia have approved both medical and recreational uses. Public opinion on marijuana has changed dramatically over the last two decades and support for legalization has doubled since 2010. However, very little is known about the prevalence and patterns of marijuana use among adults with medical conditions.
This study analyzed the 2016 and 2017 Behavioral Risk Factor Surveillance System data to report the prevalence and patterns of marijuana use among adults with self-reported medical conditions.
