MedicalResearch.com Interview with:
James S. Goodwin, M.D.
George and Cynthia Mitchell Distinguished Chair in Geriatric Medicine
University of Texas Medical Branch
Galveston, TX 77555-0177
MedicalResearch.com: What is the background for this study?
Response: Full time hospital doctors, called hospitalists, now provide the medical care for most patients hospitalized in the US. Depending on the hospital and also the hospitalist group, the working schedules of hospitalists can have vary greatly. For example, some might work 8 AM to 5PM for seven days followed by seven days off. Others might work 24 hours on and 72 hours off. Depending on the schedule of the hospitalists proving care, a patient might have one or two or three or more different doctors proving care during their stay. Some patients see a new doctor each day.
Our goal was to see if patients who received care from hospitalists who tended to work several days in a row did better than those who were cared for many different hospitalists with intermittent schedules.
James S. Goodwin, M.D.
George and Cynthia Mitchell Distinguished Chair in Geriatric Medicine
University of Texas Medical Branch
Galveston, TX 77555-0177
MedicalResearch.com: What is the background for this study?
Response: Full time hospital doctors, called hospitalists, now provide the medical care for most patients hospitalized in the US. Depending on the hospital and also the hospitalist group, the working schedules of hospitalists can have vary greatly. For example, some might work 8 AM to 5PM for seven days followed by seven days off. Others might work 24 hours on and 72 hours off. Depending on the schedule of the hospitalists proving care, a patient might have one or two or three or more different doctors proving care during their stay. Some patients see a new doctor each day.
Our goal was to see if patients who received care from hospitalists who tended to work several days in a row did better than those who were cared for many different hospitalists with intermittent schedules.
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 (
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.

