Domain Generalization for Multi-disease Detection in Fundus Photographs.

Domain generalization (DG) is a paradigm ensuring machine learning algorithms predict well on unseen domains. Recent computer vision research in DG highlighted how inconsistencies in datasets, architectures, and model criteria challenge fair comparisons. In the medical domain, the application of DG algorithms assumes an even more challenging task as medical data often exhibit significant variability due to diverse imaging modalities, patient demographics, and disease characteristics.

Comorbid Bipolar Disorder and Posttraumatic Stress Disorder: Clinical Implications and Management.

The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry.

Strategies for the Treatment of Pain in the Context of Alcohol and Substance Use Disorders.

The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss the diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry.

A systematic review of generalization research in medical image classification.

Numerous Deep Learning (DL) classification models have been developed for a large spectrum of medical image analysis applications, which promises to reshape various facets of medical practice. Despite early advances in DL model validation and implementation, which encourage healthcare institutions to adopt them, a fundamental questions remain: how can these models effectively handle domain shift? This question is crucial to limit DL models performance degradation. Medical data are dynamic and prone to domain shift, due to multiple factors.