ASTCT and USCLC Clinical Practice Recommendations for Allogeneic Stem Cell Transplant in Mycosis Fungoides and Sézary Syndrome.

Mycosis fungoides (MF) and Sézary syndrome (SS) are the most common subtypes of cutaneous T-cell lymphoma (CTCL). While MF generally follows an indolent course, a subset of patients will experience progressive and/or treatment-refractory disease; Sézary syndrome is an aggressive lymphoma associated with high morbidity and mortality. Although allogeneic hematopoietic cell transplant (allo-HCT) is the only currently available potentially curative treatment modality for MF/SS there is no published guidance on referral criteria, transplant timing orallo-HCT approach.

Evaluating the Role of Competency-Based Behavioral Interviewing in Holistic Medical School Admissions.

Problem: Holistic review is a multifaceted concept that aims to increase diversity and applicant fit with program needs by complementing traditional academic requirements with appraisal of a wider range of personal characteristics and experiences. Behavioral interviewing has been practiced and studied in human resources, business, and organizational psychology for over 50 years. Its premise is that future performance can be anticipated from past actions.

Accuracy and Reliability of Chatbot Responses to Physician Questions.

Importance: Natural language processing tools, such as ChatGPT (generative pretrained transformer, hereafter referred to as chatbot), have the potential to radically enhance the accessibility of medical information for health professionals and patients. Assessing the safety and efficacy of these tools in answering physician-generated questions is critical to determining their suitability in clinical settings, facilitating complex decision-making, and optimizing health care efficiency.

Objective: To assess the accuracy and comprehensiveness of chatbot-generated responses to physician-developed medical queries, highlighting the reliability and limitations of artificial intelligence-generated medical information.

Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model.

Background: Natural language processing models such as ChatGPT can generate text-based content and are poised to become a major information source in medicine and beyond. The accuracy and completeness of ChatGPT for medical queries is not known.

Methods: Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes/no) or descriptive answers.