Even with ChatGPT, race matters.

BACKGROUND: Applications of large language models such as ChatGPT are increasingly being studied. Before these technologies become entrenched, it is crucial to analyze whether they perpetuate racial inequities. METHODS: We asked Open AI's ChatGPT-3.5 and ChatGPT-4 to simplify 750 radiology reports with the prompt "I am a ___ patient. Simplify this radiology report:" while providing the context of the five major racial classifications on the U.S. census: White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander. To ensure an unbiased analysis, the readability scores of the outputs were calculated and compared. RESULTS: Statistically significant differences were found in both models based on the racial context. For ChatGPT-3.5, output for White and Asian was at a significantly higher reading grade level than both Black or African American and American Indian or Alaska Native, among other differences. For ChatGPT-4, output for Asian was at a significantly higher reading grade level than American Indian or Alaska Native and Native Hawaiian or other Pacific Islander, among other differences. CONCLUSION: Here, we tested an application where we would expect no differences in output based on racial classification. Hence, the differences found are alarming and demonstrate that the medical community must remain vigilant to ensure large language models do not provide biased or otherwise harmful outputs.

The disproportionate impact of peer learning on emergency radiology.

PURPOSE: The use of peer learning methods in radiology continues to grow as a means to constructively learn from past mistakes. This study examined whether emergency radiologists receive a disproportionate amount of peer learning feedback entered as potential learning opportunities (PLO), which could play a significant role in stress and career satisfaction. Our institution offers 24/7 attending coverage, with emergency radiologists interpreting a wide range of X-ray, ultrasound and CT exams on both adults and pediatric patients. MATERIALS AND METHODS: Peer learning submissions entered as PLO at a single large academic medical center over a span of 3 years were assessed by subspecialty distribution and correlated with the number of attending radiologists in each section. Total number of studies performed on emergency department patients and throughout the hospital system were obtained for comparison purposes. Data was assessed using analysis of variance and post hoc analysis. RESULTS: Emergency radiologists received significantly more (2.5 times) PLO submissions than the next closest subspeciality division and received more yearly PLO submissions per attending compared to other subspeciality divisions. This was found to still be true when normalizing for increased case volumes; Emergency radiologists received more PLO submissions per 1000 studies compared to other divisions in our department (1.59 vs. 0.85, p = 0.04). CONCLUSION: Emergency radiologists were found to receive significantly more PLO submissions than their non-emergency colleagues. Presumed causes for this discrepancy may include a higher error rate secondary to wider range of studies interpreted, demand for shorter turn-around times, higher volumes of exams read per shift, and hindsight bias in the setting of follow-up review.

Understanding Bias in Artificial Intelligence: A Practice Perspective.

In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Society of Neuroradiology (ASNR) Diversity and Inclusion Committee, focused on expanding the understanding of bias in artificial intelligence, with a health equity lens, and provided key concepts for neuroradiologists to approach the evaluation of these tools. In this perspective, we distill key parts of this discussion, including understanding why this topic is important to neuroradiologists and lending insight on how neuroradiologists can develop a framework to assess health equity-related bias in artificial intelligence tools. In addition, we provide examples of clinical workflow implementation of these tools so that we can begin to see how artificial intelligence tools will impact discourse on equitable radiologic care. As continuous learners, we must be engaged in new and rapidly evolving technologies that emerge in our field. The Diversity and Inclusion Committee of the ASNR has addressed this subject matter through its programming content revolving around health equity in neuroradiologic advances.

The Remote Academic Radiologist: AJR Expert Panel Narrative Review.

The importance of developing a robust remote workforce in academic radiology has come to the forefront due to several converging factors. COVID-19, and the abrupt transformation it precipitated in terms of how radiologists worked, has been the biggest impetus for change; concurrent factors such as increasing examination volumes and radiologist burnout have also contributed. How to best advance the most desirable and favorable aspects of remote work while preserving an academic environment that fulfills the tripartite mission is a critical challenge that nearly all academic institutions face today. In this article, we discuss current challenges in academic radiology, including effects of the COVID-19 pandemic, from three perspectives-the radiologist, the learner, and the health system-addressing the following topics: productivity, recruitment, wellness, clinical supervision, mentorship and research, educational engagement, radiologist access, investments in technology, and radiologist value. Throughout, we focus on the opportunities and drawbacks of remote work, to help guide its effective and reliable integration into academic radiology practices.

Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI.

Artificial intelligence (AI) holds promise for helping patients access new and individualized health care pathways while increasing efficiencies for health care practitioners. Radiology has been at the forefront of this technology in medicine; many radiology practices are implementing and trialing AI-focused products. AI also holds great promise for reducing health disparities and promoting health equity. Radiology is ideally positioned to help reduce disparities given its central and critical role in patient care. The purposes of this article are to discuss the potential benefits and pitfalls of deploying AI algorithms in radiology, specifically highlighting the impact of AI on health equity; to explore ways to mitigate drivers of inequity; and to enhance pathways for creating better health care for all individuals, centering on a practical framework that helps radiologists address health equity during deployment of new tools.

White Paper: Best Practices in the Communication and Management of Actionable Incidental Findings in Emergency Department Imaging.

PURPOSE: Actionable incidental findings (AIFs) are common in radiologic imaging. Imaging is commonly performed in emergency department (ED) visits, and AIFs are frequently encountered, but the ED presents unique challenges for communication and follow-up of these findings. The authors formed a multidisciplinary panel to seek consensus regarding best practices in the reporting, communication, and follow-up of AIFs on ED imaging tests. METHODS: A 15-member panel was formed, nominated by the ACR and American College of Emergency Physicians, to represent radiologists, emergency physicians, patients, and those involved in health care systems and quality. A modified Delphi process was used to identify areas of best practice and seek consensus. The panel identified four areas: (1) report elements and structure, (2) communication of findings with patients, (3) communication of findings with clinicians, and (4) follow-up and tracking systems. A survey was constructed to seek consensus and was anonymously administered in two rounds, with a priori agreement requiring at least 80% consensus. Discussion occurred after the first round, with readministration of questions where consensus was not initially achieved. RESULTS: Consensus was reached in the four areas identified. There was particularly strong consensus that AIFs represent a system-level issue, with need for approaches that do not depend on individual clinicians or patients to ensure communication and completion of recommended follow-up. CONCLUSIONS: This multidisciplinary collaboration represents consensus results on best practices regarding the reporting and communication of AIFs in the ED setting.

Change Management Strategies Toward Dismantling Race-Based Structural Barriers in Radiology.

Political momentum for antiracist policies grew out of the collective trauma highlighted during the COVID pandemic. This prompted discussions of root cause analyses for differences in health outcomes among historically underserved populations, including racial and ethnic minorities. Dismantling structural racism in medicine is an ambitious goal that requires widespread buy-in and transdisciplinary collaborations across institutions to establish systematic, rigorous approaches that enable sustainable change. Radiology is at the center of medical care and renewed focus on equity, diversity, and inclusion (EDI) provides an opportune window for radiologists to facilitate an open forum to address racialized medicine to catalyze real and lasting change. The framework of change management can help radiology practices create and maintain this change while minimizing disruption. This article discusses how change management principles can be leveraged by radiology to lead EDI interventions that will encourage honest dialogue, serve as a platform to support institutional EDI efforts, and lead to systemic change.

Quality Improvement Report: Improving Pre- and Postprocedure Care Area Workflows at a Busy Urban Academic Hospital Using Lean Management Principles.

Radiology procedure workflow is a summation of individual workflows for scheduling, precertification, preprocedure clinic visits, and day of procedure, representing a complex total process with many opportunities for inefficiencies and waste. At the authors' institution, a lack of standard work and communication gaps in a pre- and postprocedure care area (PPCA) workflow were identified as factors in bottlenecks, waits and delays, and staff and patient frustrations. Using "lean" process improvement tools, these workflows were targeted in a rapid improvement event (RIE). A cross-functional team was formed to work on the PPCA workflow RIE. Using lean management principles, process gaps were identified and changes were instituted to improve patient and information flow. Three projects were implemented over a course of 4 months. These included a 5S, a lean methodology of workplace organization to optimize supply cabinets; standardization of nursing preprocedure documentation and process; and standard work confirmation in daily management system huddles. At baseline, 45% of patients were prepared within 60 minutes of their arrival in the PPCA. After the RIE and instituting the changes from the RIE, 80% of patients were prepared within 60 minutes of their arrival in the PPCA. Implementing lean management strategies, such as daily management systems and huddles, and establishing standard work confirmation help to eliminate waste and create systems and teams that sustain and improve complex workflows. © RSNA, 2022.

Natural Language Processing Model for Identifying Critical Findings-A Multi-Institutional Study.

Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the full report and identify all the pertinent information for prioritizing the critical cases. We developed an automated NLP pipeline using a transformer-based ClinicalBERT++ model which was fine-tuned on 3 M radiology reports and compared against the traditional BERT model. We validated the models on both internal hold-out ED cases from EUH as well as external cases from Mayo Clinic. We also evaluated the model by combining different sections of the radiology reports. On the internal test set of 3819 reports, the ClinicalBERT++ model achieved 0.96 f1-score while the BERT also achieved the same performance using the reason for exam and impression sections. However, ClinicalBERT++ outperformed BERT on the external test dataset of 2039 reports and achieved the highest performance for classifying critical finding reports (0.81 precision and 0.54 recall). The ClinicalBERT++ model has been successfully applied to large-scale radiology reports from 5 different sites. Automated NLP system that can analyze free-text radiology reports, along with the reason for the exam, to identify critical radiology findings and recommendations could enable automated alert notifications to clinicians about the need for clinical follow-up. The clinical significance of our proposed model is that it could be used as an additional layer of safeguard to clinical practice and reduce the chance of important findings reported in a radiology report is not overlooked by clinicians as well as provide a way to retrospectively track large hospital databases for evaluating the documentation of the critical findings.

Gender Disparity in Surgical Device Patents: A Five-year Trend From Canada and the United States.

INTRODUCTION: Despite an increasing number of women pursuing careers in science, engineering, and medicine, gender disparities in patents persist. This study sought to analyze trends in inventor's gender for surgical device patents filed and granted in Canada and the United States from 2015 to 2019. METHODS: This study analyzed patents filed and granted by the Canadian Intellectual Property Office (CIPO) in the category of "Diagnosis; Surgery; Identification" and the United States Patent and Trademark Office (USPTO) in the category of "Surgery" from 2015 to 2019. The gender of the patent applicants was determined using a gender algorithm that predicts gender based on first names. Gender matches with names having a probability of less than 95% were excluded. RESULTS: We identified 14,312 inventors on patents filed and 12,737 inventors on patents granted by the CIPO for "Diagnosis; Surgery; Identification". In the USPTO category of "Surgery," we identified 75,890 inventors on patents filed and 44,842 inventors on patents granted. Female inventors accounted for 7%-10% of inventors from 2015 to 2019 for both patents filed and granted. The proportion of female inventors on patents granted was significantly lower than for patents filed for four of the 5 y analyzed for both the USPTO and CIPO. CONCLUSIONS: Female representation in surgical device patenting has stagnated, between 7 and 10%, from 2015 to 2019 in Canada and the United States. This underrepresentation of female inventors in surgical device patenting represents sizable gender disparity.

Prenatal Oxygen and Glucose Therapy Normalizes Insulin Secretion and Action in Growth-Restricted Fetal Sheep.

Placental insufficiency (PI) lowers fetal oxygen and glucose concentrations, which disrupts glucose-insulin homeostasis and promotes fetal growth restriction (FGR). To date, prenatal treatments for FGR have not attempted to correct the oxygen and glucose supply simultaneously. Therefore, we investigated whether a 5-day correction of oxygen and glucose concentrations in PI-FGR fetuses would normalize insulin secretion and glucose metabolism. Experiments were performed in near-term FGR fetal sheep with maternal hyperthermia-induced PI. Fetal arterial oxygen tension was increased to normal levels by increasing the maternal inspired oxygen fraction and glucose was infused into FGR fetuses (FGR-OG). FGR-OG fetuses were compared with maternal air insufflated, saline-infused fetuses (FGR-AS) and control fetuses. Prior to treatment, FGR fetuses were hypoxemic and hypoglycemic and had reduced glucose-stimulated insulin secretion (GSIS). During treatment, oxygen, glucose, and insulin concentrations increased, and norepinephrine concentrations decreased in FGR-OG fetuses, whereas FGR-AS fetuses were unaffected. On treatment day 4, glucose fluxes were measured with euglycemic and hyperinsulinemic-euglycemic clamps. During both clamps, rates of glucose utilization and production were greater in FGR-AS than FGR-OG fetuses, while glucose fluxes in FGR-OG fetuses were not different than control rates. After 5 days of treatment, GSIS increased in FGR-OG fetuses to control levels and their ex vivo islet GSIS was greater than FGR-AS islets. Despite normalization in fetal characteristics, GSIS, and glucose fluxes, FGR-OG and FGR-AS fetuses weighed less than controls. These findings show that sustained, simultaneous correction of oxygen and glucose normalized GSIS and whole-body glucose fluxes in PI-FGR fetuses after the onset of FGR.