Artificial Intelligence and Its Effect on Radiology Residency Education: Current Challenges, Opportunities, and Future Directions.

Artificial intelligence has become an impressive force manifesting itself in the radiology field, improving workflows, and influencing clinical decision making. With this increasing presence, a closer look at how residents can be properly exposed to this technology is needed. Within this article, we aim to discuss the three pillars central to a trainee's experience including education on AI, AI education tools, and clinical implementation of AI.

Enhancing radiology training with GPT-4: Pilot analysis of automated feedback in trainee preliminary reports.

Rationale And Objectives: Radiology residents often receive limited feedback on preliminary reports issued during independent call. This study aimed to determine if Large Language Models (LLMs) can supplement traditional feedback by identifying missed diagnoses in radiology residents' preliminary reports.

Materials & Methods: A randomly selected subset of 500 (250 train/250 validation) paired preliminary and final reports between 12/17/2022 and 5/22/2023 were extracted and de-identified from our institutional database.

A Fully Collaborative, Noteless Electronic Medical Record Designed to Minimize Information Chaos: Software Design and Feasibility Study.

Background: Clinicians spend large amounts of their workday using electronic medical records (EMRs). Poorly designed documentation systems contribute to the proliferation of out-of-date information, increased time spent on medical records, clinician burnout, and medical errors. Beyond software interfaces, examining the underlying paradigms and organizational structures for clinical information may provide insights into ways to improve documentation systems.

Beyond Notes: Why It Is Time to Abandon an Outdated Documentation Paradigm.

Clinicians spend a substantial part of their workday reviewing and writing electronic medical notes. Here we describe how the current, widely accepted paradigm for electronic medical notes represents a poor organizational framework for both the individual clinician and the broader medical team. As described in this viewpoint, the medical chart-including notes, labs, and imaging results-can be reconceptualized as a dynamic, fully collaborative workspace organized by topic rather than time, writer, or data type.

A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.

Background: Incidental radiographic findings, such as adrenal nodules, are commonly identified in imaging studies and documented in radiology reports. However, patients with such findings frequently do not receive appropriate follow-up, partially due to the lack of tools for the management of such findings and the time required to maintain up-to-date lists. Natural language processing (NLP) is capable of extracting information from free-text clinical documents and could provide the basis for software solutions that do not require changes to clinical workflows.

Diagnostic Imaging Modalities to Assess Treatment Response of Bone Metastasis in Patients Receiving Palliative Radiotherapy: A Scoping Review of the Literature.

Purpose: Several studies have used objective radiologic data to assess the effect of palliative radiotherapy on tumor burden. The purpose of this literature review was to survey the various metrics that have been used to quantify bone tumor response to palliative radiotherapy by radiographical means and to determine whether any of these metrics were associated with clinical palliative outcomes.

Methods: In accordance with PRISMA extension for Scoping Reviews guidelines, a literature search Ovid Medline and OldMedline from 1946 to February 6, 2019, Embase Classic/Embase from 1947 to 2019 week 5, and Cochrane Central Register of Controlled Trials February 2019 to extract articles related to quantified radiologic evaluation of bone metastases following palliative radiotherapy.

Task definition, annotated dataset, and supervised natural language processing models for symptom extraction from unstructured clinical notes.

Introduction: Machine learning (ML) and natural language processing have great potential to improve information extraction (IE) within electronic medical records (EMRs) for a wide variety of clinical search and summarization tools. Despite ML advancements, clinical adoption of real time IE tools for patient care remains low. Clinically motivated IE task definitions, publicly available annotated clinical datasets, and inclusion of subtasks such as coreference resolution and named entity normalization are critical for the development of useful clinical tools.

Mortality Outcomes With Intensive Blood Pressure Targets in Chronic Kidney Disease Patients.

Hypertension is highly prevalent and morbid in the chronic kidney disease population, and blood pressure (BP) targets for this population are unclear. We aimed to compare all-cause mortality outcomes with intensively targeting systolic BP to <130 mm Hg versus a standard of <140 mm Hg. Individual patient data from 4983 chronic kidney disease patients with hypertension were pooled from 4 multicenter randomized control trials-AASK (African American Study of Kidney Disease and Hypertension), ACCORD (Action to Control Cardiovascular Risk in Diabetes), MDRD (Modification of Diet in Renal Disease), and the SPRINT (Systolic Blood Pressure Intervention Trial).

Evaluating the impact of translated written discharge instructions for patients with limited English language proficiency.

Introduction: Patients with limited English language proficiency have indicated that they believe post-operative instructions written in their native language will improve comprehension over verbal translation alone, but the effect of this has not been previously studied. We hypothesize that providing written discharge instructions in Spanish for native Spanish speakers will improve comprehension regarding post-operative care after routine otolaryngologic procedures when compared to instructions written in English.

Methods: This prospective randomized controlled trial enrolled subjects who met criteria from June 2016 to November 2016.