The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.

The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.

No abstract

Background The Coronavirus Disease 2019 (COVID-19) pandemic has necessitated a sudden transition to remote learning in medical schools. We aimed to assess perceptions of remote learning among pre-clinical medical students and subsequently to identify pros and cons of remote learning, as well as uncover gaps to address in ongoing curricular development. Methods A survey was distributed to first- and second-year medical students at the University of California San Diego School of Medicine in March 2020. Frequencies of responses to structured multiple-choice questions were compared regarding impacts of remote learning on quality of instruction and ability to participate, value of various remote learning resources, living environment, and preparedness for subsequent stages of training. Responses to open-ended questions about strengths and weaknesses of the remote curriculum and overall reflections were coded for thematic content. Results Of 268 students enrolled, 104 responded (53.7% of first-year students and 23.9% of second-year students). Overall, students felt that remote learning had negatively affected the quality of instruction and their ability to participate. Most (64.1%) preferred the flexibility of learning material at their own pace. Only 25.5% of respondents still felt connected to the medical school or classmates, and feelings of anxiety and isolation were noted negatives of remote learning. Most second-year students (56.7%) felt their preparation for the United States Medical Licensing Examination Step 1 exam was negatively affected, and 43.3% felt unprepared to begin clerkships. In narrative responses, most students appreciated the increased flexibility of remote learning, but they also identified several deficits that still need to be addressed, including digital fatigue, decreased ability to participate, and lack of clinical skills, laboratory, and hands-on learning. Conclusions Videocasted lectures uploaded in advance, electronic health record and telehealth training for students, and training for teaching faculty to increase technological fluency may be considered to optimize remote learning curricula.