Author Interviews, Dermatology, JAMA, Technology / 28.04.2021

MedicalResearch.com Interview with: Yun Liu, PhD Google Health Palo Alto, California MedicalResearch.com: What is the background for this study? Would you describe the system? Does it use dermatoscopic images? Response: Dermatologic conditions are extremely common and a leading cause of morbidity worldwide. Due to limited access to dermatologists, patients often first seek help from non-specialists. However, non-specialists have been reported to have lower diagnostic accuracies compared to dermatologists, which may impact the quality of care. In this study, we built upon prior work published in Nature Medicine, where we developed a computer algorithm (a deep learning system, DLS) to interpret de-identified clinical images of skin conditions and associated medical history (such as whether the patient reported a history of psoriasis). These clinical images are taken using consumer-grade hardware such as point-and-shoot cameras and tablets, which we felt was a more accessible and widely-available device compared to dermatoscopes. Given such images of the skin condition as input, the DLS outputs a differential diagnosis, which is a rank-ordered list of potential matching skin conditions. In this paper, we worked with user experience researchers to create an artificial intelligence (AI) tool based on this DLS. The tool was designed to provide clinicians with additional information per skin condition prediction, such as textual descriptions, similar-appearing conditions, and the typical clinical workup for the condition. We then conducted a randomized study where 40 clinicians (20 primary care physicians, 20 nurse practitioners) reviewed over 1,000 cases -- with half the cases with the AI-based assistive tool, and half the cases without. For each case, the reference diagnosis was based on a panel of 3 dermatologists. (more…)
Author Interviews, Cancer Research, JAMA, Prostate Cancer, Technology / 13.11.2020

MedicalResearch.com Interview with: Dave Steiner MD PhD Clinical Research Scientist Google Health, Palo Alto, California MedicalResearch.com: What is the background for this study? Response: For prostate cancer patients, the grading of cancer in prostate biopsies by pathologists is central to risk stratification and treatment decisions. However, the grading process can be subjective, often resulting in variability among pathologists. This variability can complicate diagnostic and treatment decisions. As an initial step towards addressing this problem, we and others in the field have recently developed artificial intelligence (AI) algorithms that perform on-par with expert pathologists for prostate cancer grading. Such algorithms have the potential to improve the quality and efficiency of prostate biopsy grading, but the impact of these algorithms when used by pathologists has not been well studied. In the current study, we developed and evaluated an AI-based assistant tool for use by pathologists while reviewing prostate biopsies. (more…)
Author Interviews, Medical Imaging, Technology / 11.12.2019

MedicalResearch.com Interview with: Dr. David Steiner, MD PhD Google Health, USA MedicalResearch.com: What is the background for this study? Response: Advances in artificial intelligence raise promising opportunities for improved interpretation of chest X-rays and many other types of medical images. However, even before researchers begin to address the critical question of clinical validation, there is important work to be done establishing strategies for evaluating and comparing different artificial intelligence algorithms. One challenge is defining and collecting the correct clinical interpretation or “label” for the large number of chest X-rays needed to train and evaluate these algorithms. Another important challenge is evaluating the algorithm on a dataset that actually represents the diversity of the cases encountered in clinical practice. For example, it might be relatively easy to make an algorithm that performs perfectly on a few hundred or so “easy” cases, but this of course might not be particularly useful in practice. (more…)