Long-term stability in perovskite solar cells through atomic layer deposition of tin oxide.

Robust contact schemes that boost stability and simplify the production process are needed for perovskite solar cells (PSCs). We codeposited perovskite and hole-selective contact while protecting the perovskite to enable deposition of SnOx/Ag without the use of a fullerene. The SnOx, prepared through atomic layer deposition, serves as a durable inorganic electron transport layer. Tailoring the oxygen vacancy defects in the SnOx layer led to power conversion efficiencies (PCEs) of >25%. Our devices exhibit superior stability over conventional p-i-n PSCs, successfully meeting several benchmark stability tests. They retained >95% PCE after 2000 hours of continuous operation at their maximum power point under simulated AM1.5 illumination at 65°C. Additionally, they boast a certified T97 lifetime exceeding 1000 hours.

ChatGPT and Large-Language Models in Radiology: Perspectives From the Field.

Generative artificial intelligence (AI) and large language-models (LLMs) are increasingly being recognized as tools that can potentially transform many industries, including health care. The implementation and use of these tools among radiologists is likely variable, driven by radiology practice and institutional factors. Radiologists from various practices were asked about their perspectives on generative AI and LLMs in radiology.

ACR Appropriateness Criteria® Dizziness and Ataxia: 2023 Update.

Diagnostic evaluation of a patient with dizziness or vertigo is complicated by a lack of standardized nomenclature, significant overlap in symptom descriptions, and the subjective nature of the patient's symptoms. Although dizziness is an imprecise term often used by patients to describe a feeling of being off-balance, in many cases dizziness can be subcategorized based on symptomatology as vertigo (false sense of motion or spinning), disequilibrium (imbalance with gait instability), presyncope (nearly fainting or blacking out), or lightheadedness (nonspecific). As such, current diagnostic paradigms focus on timing, triggers, and associated symptoms rather than subjective descriptions of dizziness type. Regardless, these factors complicate the selection of appropriate diagnostic imaging in patients presenting with dizziness or vertigo. This document serves to aid providers in this selection by using a framework of definable clinical variants. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Cerebrovascular Diseases-Stroke and Stroke-Related Conditions.

Cerebrovascular disease encompasses a vast array of conditions. The imaging recommendations for stroke-related conditions involving noninflammatory steno-occlusive arterial and venous cerebrovascular disease including carotid stenosis, carotid dissection, intracranial large vessel occlusion, and cerebral venous sinus thrombosis are encompassed by this document. Additional imaging recommendations regarding complications of these conditions including intraparenchymal hemorrhage and completed ischemic strokes are also discussed. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

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.