Common Questions Research Articles

Forensic Pathologist Testimony, Part 1: Common Questions and Considerations.

Forensic pathologists are the only medical specialists who routinely testify in courts of law as part of their occupation. As such, one element of their training involves learning how to testify responsibly and competently. Individuals qualified as experts are permitted to offer opinions in order to assist the triers of fact. We review the forensic pathologist's primary role as an expert witness which is to lay out concepts clearly, methodically, and objectively, in medicine, pathophysiology, toxicology, and injury, to convey these concepts with understandable, plain language, and to explain how certain evidence, physical, medical, and historical, supports their conclusions and/or presents limitations. We discuss various components about testifying in court, some common expert questions posed to the forensic pathologist, and what concepts and facts they might consider when answering them. We do not offer a prescription as to how one should answer these questions specifically, but rather we provide a single resource that summarizes some of the key issues that may arise in court for the forensic pathologist.

Enhancement of the Performance of Large Language Models in Diabetes Education through Retrieval-Augmented Generation: Comparative Study.

Large language models (LLMs) demonstrated advanced performance in processing clinical information. However, commercially available LLMs lack specialized medical knowledge and remain susceptible to generating inaccurate information. Given the need for self-management in diabetes, patients commonly seek information online. We introduce the Retrieval-augmented Information System for Enhancement (RISE) framework and evaluate its performance in enhancing LLMs to provide accurate responses to diabetes-related inquiries. This study aimed to evaluate the potential of the RISE framework, an information retrieval and augmentation tool, to improve the LLM's performance to accurately and safely respond to diabetes-related inquiries. The RISE, an innovative retrieval augmentation framework, comprises 4 steps: rewriting query, information retrieval, summarization, and execution. Using a set of 43 common diabetes-related questions, we evaluated 3 base LLMs (GPT-4, Anthropic Claude 2, Google Bard) and their RISE-enhanced versions respectively. Assessments were conducted by clinicians for accuracy and comprehensiveness and by patients for understandability. The integration of RISE significantly improved the accuracy and comprehensiveness of responses from all 3 base LLMs. On average, the percentage of accurate responses increased by 12% (15/129) with RISE. Specifically, the rates of accurate responses increased by 7% (3/43) for GPT-4, 19% (8/43) for Claude 2, and 9% (4/43) for Google Bard. The framework also enhanced response comprehensiveness, with mean scores improving by 0.44 (SD 0.10). Understandability was also enhanced by 0.19 (SD 0.13) on average. Data collection was conducted from September 30, 2023 to February 5, 2024. The RISE significantly improves LLMs' performance in responding to diabetes-related inquiries, enhancing accuracy, comprehensiveness, and understandability. These improvements have crucial implications for RISE's future role in patient education and chronic illness self-management, which contributes to relieving medical resource pressures and raising public awareness of medical knowledge.

Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study.

Recent surveys indicate that 48% of consumers actively use generative artificial intelligence (AI) for health-related inquiries. Despite widespread adoption and the potential to improve health care access, scant research examines the performance of AI chatbot responses regarding emergency care advice. We assessed the quality of AI chatbot responses to common emergency care questions. We sought to determine qualitative differences in responses from 4 free-access AI chatbots, for 10 different serious and benign emergency conditions. We created 10 emergency care questions that we fed into the free-access versions of ChatGPT 3.5 (OpenAI), Google Bard, Bing AI Chat (Microsoft), and Claude AI (Anthropic) on November 26, 2023. Each response was graded by 5 board-certified emergency medicine (EM) faculty for 8 domains of percentage accuracy, presence of dangerous information, factual accuracy, clarity, completeness, understandability, source reliability, and source relevancy. We determined the correct, complete response to the 10 questions from reputable and scholarly emergency medical references. These were compiled by an EM resident physician. For the readability of the chatbot responses, we used the Flesch-Kincaid Grade Level of each response from readability statistics embedded in Microsoft Word. Differences between chatbots were determined by the chi-square test. Each of the 4 chatbots' responses to the 10 clinical questions were scored across 8 domains by 5 EM faculty, for 400 assessments for each chatbot. Together, the 4 chatbots had the best performance in clarity and understandability (both 85%), intermediate performance in accuracy and completeness (both 50%), and poor performance (10%) for source relevance and reliability (mostly unreported). Chatbots contained dangerous information in 5% to 35% of responses, with no statistical difference between chatbots on this metric (P=.24). ChatGPT, Google Bard, and Claud AI had similar performances across 6 out of 8 domains. Only Bing AI performed better with more identified or relevant sources (40%; the others had 0%-10%). Flesch-Kincaid Reading level was 7.7-8.9 grade for all chatbots, except ChatGPT at 10.8, which were all too advanced for average emergency patients. Responses included both dangerous (eg, starting cardiopulmonary resuscitation with no pulse check) and generally inappropriate advice (eg, loosening the collar to improve breathing without evidence of airway compromise). AI chatbots, though ubiquitous, have significant deficiencies in EM patient advice, despite relatively consistent performance. Information for when to seek urgent or emergent care is frequently incomplete and inaccurate, and patients may be unaware of misinformation. Sources are not generally provided. Patients who use AI to guide health care decisions assume potential risks. AI chatbots for health should be subject to further research, refinement, and regulation. We strongly recommend proper medical consultation to prevent potential adverse outcomes.

Assessing ChatGPT Responses to Common Patient Questions on Knee Osteoarthritis

Assessing ChatGPT Responses to Common Patient Questions on Knee Osteoarthritis

Evaluating ChatGPT's performance in answering common patient questions on cervical cancer

Evaluating ChatGPT's performance in answering common patient questions on cervical cancer

Assessing ChatGPT's performance in answering common questions regarding endometrial cancer

Assessing ChatGPT's performance in answering common questions regarding endometrial cancer

Contemporary Tibetan Monastic Business and the Corporate Form

In early twenty-first century Tibet, many Geluk monasteries established capital funds that they invested in small businesses—the Geluk being one of the main Tibetan Buddhist traditions. Drawing on fieldwork in northeast Tibet, this article examines debates surrounding this monastic engagement in business, asking why it proved contentious despite historical and canonical precedent. I approach this question by thinking about monastic business engagement in relation to Geluk monasticism as a distinctive corporate form. Foregrounding how monks made sense of what they were doing as corporate actors, I examine common rationales, concerns, and questions of legitimacy at the heart of arguments both for and against monastic business development, and how these related to the monastic corporation’s core concern with preservation and continuity. The article concludes with some broader reflections on what these debates can tell us about the ongoing process of corporation-making, suggesting some possible avenues for future research.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

Assessment of Artificial Intelligence Chatbot Responses to Common Patient Questions on Bone Sarcoma.

The potential impacts of artificial intelligence (AI) chatbots on care for patients with bone sarcoma is poorly understood. Elucidating potential risks and benefits would allow surgeons to define appropriate roles for these tools in clinical care. Eleven questions on bone sarcoma diagnosis, treatment, and recovery were inputted into three AI chatbots. Answers were assessed on a 5-point Likert scale for five clinical accuracy metrics: relevance to the question, balance and lack of bias, basis on established data, factual accuracy, and completeness in scope. Responses were quantitatively assessed for empathy and readability. The Patient Education Materials Assessment Tool (PEMAT) was assessed for understandability and actionability. Chatbots scored highly on relevance (4.24) and balance/lack of bias (4.09) but lower on basing responses on established data (3.77), completeness (3.68), and factual accuracy (3.66). Responses generally scored well on understandability (84.30%), while actionability scores were low for questions on treatment (64.58%) and recovery (60.64%). GPT-4 exhibited the highest empathy (4.12). Readability scores averaged between 10.28 for diagnosis questions to 11.65 for recovery questions. While AI chatbots are promising tools, current limitations in factual accuracy and completeness, as well as concerns of inaccessibility to populations with lower health literacy, may significantly limit their clinical utility.

5.E. Round table: Crafting Policies for Health Promotion: evidence from local settings

Abstract As communities vary in their needs, challenges, and resources, tailoring health promotion policies to specific contexts becomes imperative for effectiveness. Drawing upon the expertise of seasoned practitioners, policymakers, and researchers, this workshop offers a multifaceted perspective on navigating the complexities inherent in designing, implementing, and evaluating locally tailored health promotion policies, with a specific focus on environmental and social factors. The workshop will make it possible to compare different experiences from various European countries, with high-level speakers who have different backgrounds and roles in their countries and regions, thus providing an interesting and interactive way of comparing and examining real-world examples and best practices. The examples provided delve into: participatory techniques to design healthier environments, to identifying, collecting and elaborating evidence upon which to build policies and actions that are effective in addressing specific health challenges. Through the examples presented, we will seek to make the case for multi-actor and multisectoral collaboration. In addition, we will try to understand How do subnational authorities and partners identify priorities for action. A common question asked to the speakers will therefore: how can a variety of actors (local and regional authorities, university and research centres...) come together to support cross-sectoral action? How can policymakers be supported towards creating environments that prioritize the well-being of all, ensuring a healthier and more prosperous future for everyone? The end goal will be to enable attendees to be equipped with actionable strategies and tools to foster meaningful change within their own communities, by understanding the intricate interplay between community dynamics, stakeholder engagement, evidence-based practices, and policy implementation strategies. The workshop is being organized by the WHO Regions for Health Network (RHN). Key messages • Tailoring health promotion policies to specific contexts is imperative for effectiveness, considering the diverse needs, challenges, and resources of different communities. • Subnational authorities and partners must identify priorities for action and collaborate across sectors to address specific health challenges effectively. Speakers/Panelists Klara Dokova Medical University of Varna, Varna, Bulgaria Camilla Ihlebaek Norwegian University of Life Sciences, Aas, Norway Jo Peden Public Health Wales, Cardiff, UK Odile Mekel NRW Centre for Health, Bochum, Germany Liesbeth van Holten Province of Utrecht, Utrecht, Netherlands

Evaluating the reliability of the responses of large language models to keratoconus-related questions

ABSTRACT Clinical relevance Artificial intelligence has undergone a rapid evolution and large language models (LLMs) have become promising tools for healthcare, with the ability of providing human-like responses to questions. The capabilities of these tools in addressing questions related to keratoconus (KCN) have not been previously explored. Background In this study, the responses were evaluated from three LLMs – ChatGPT-4, Copilot, and Gemini – to common patient questions regarding KCN. Methods Fifty real-life patient inquiries regarding general information, aetiology, symptoms and diagnosis, progression, and treatment of KCN were presented to the LLMs. Evaluations of the answers were conducted by three ophthalmologists with a 5-point Likert scale ranging from ‘strongly disagreed’ to ‘strongly agreed’. The reliability of the responses provided by LLMs was evaluated using the DISCERN and the Ensuring Quality Information for Patients (EQIP) scales. Readability metrics (Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Coleman-Liau Index) were calculated to evaluate the complexity of responses. Results ChatGPT-4 consistently scored 3 points or higher for all (100%) its responses, while Copilot had five (10%) and Gemini had two (4%) responses scoring 2 points or below. ChatGPT-4 achieved a ‘strongly agree’ rate of 74% across all questions, markedly superior to Copilot at 34% and Gemini at 42% (p < 0.001); and recorded the highest ‘strongly agree’ rates in general information and symptoms & diagnosis categories (90% for both). The median Likert scores differed among LLMs (p < 0.001), with ChatGPT-4 scoring highest and Copilot scoring lowest. Although ChatGPT-4 exhibited more reliability based on the DISCERN scale, it was characterised by lower readability and higher complexity. While all LLMs provided responses categorised as ‘extremely difficult to read’, the responses provided by Copilot showed higher readability. Conclusions Despite the responses provided by ChatGPT-4 exhibiting lower readability and greater complexity, it emerged as the most proficient in answering KCN-related questions.

Comparative Analysis of the Accuracy of Large Language Models in Addressing Common Pulmonary Embolism Patient Questions.

Comparative Analysis of the Accuracy of Large Language Models in Addressing Common Pulmonary Embolism Patient Questions.

Social media users’ perspectives of spinal cord stimulation: an analysis of data sourced from social media

ObjectivesTo identify Reddit users’ viewpoints and inquiries about spinal cord stimulation (SCS) for chronic back pain using Reddit databases.MethodsWe performed a qualitative analysis of public, anonymous threads and comments from...

Trends in CT examination utilization in the emergency department during and after the COVID-19 pandemic.

The increasing use of CT imaging in emergency departments, despite efforts of reducing low-value imaging, is not fully understood, especially during and after the COVID-19 pandemic. The aim of this study was to investigate the impact of COVID-19 pandemic related measures on trends and volume in CT examinations requested in the emergency department. CT examinations of the head, chest, and/or abdomen-pelvis (n = 161,008), and chest radiographs (n = 113,240) performed at our tertiary care hospital between 01/2014 and 12/2023 were retrospectively analyzed. CT examinations (head, chest, abdomen, dual-region and polytrauma) and chest radiographs requested by the emergency department during (03/2020-03/2022) and after the COVID-19 pandemic (04/2022-12/2023) were compared to a pre-pandemic control period (02/2018-02/2020). Analyses included CT examinations per emergency department visit, and prediction models based on pre-pandemic trends and inpatient data. A regular expressions text search algorithm determined the most common clinical questions. The usage of dual-region and chest CT examinations were higher during (+ 116,4% and + 115.8%, respectively; p < .001) and after the COVID-19 pandemic (+ 88,4% and + 70.7%, respectively; p < .001), compared to the control period. Chest radiograph usage decreased (-54.1% and - 36.4%, respectively; p < .001). The post-pandemic overall CT examination rate per emergency department visit increased by 4.7%. The prediction model underestimated (p < .001) the growth (dual-region CT: 22.3%, chest CT: 26.7%, chest radiographs: -30.4%), and the rise (p < .001) was higher compared to inpatient data (dual-region CT: 54.8%, chest CT: 52.0%, CR: -32.3%). Post-pandemic, the number of clinical questions to rule out "pulmonary infiltrates", "abdominal pain" and "infection focus" increased up to 235.7% compared to the control period. Following the COVID-19 pandemic, chest CT and dual-region CT usage in the emergency department experienced a disproportionate and sustained surge compared to pre-pandemic growth.

Analyzing the precision and readability of a healthcare focused artificial intelligence platform on common questions regarding breast augmentation

Aim: The purpose of this study was to determine the quality and accessibility of the outputs from a healthcare-specific artificial intelligence (AI) platform for common questions during the perioperative period for a common plastic surgery procedure. Methods: Doximity GPT (Doximity, San Francisco, CA) and ChatGPT 3.5 (OpenAI, San Francisco, CA) were utilized to search 20 common perioperative patient inquiries regarding breast augmentation. The structure, content, and readability of responses were compared using t -tests and chi-square tests, with P &lt; 0.05 used as the cutoff for significance. Results: Out of 80 total AI-generated outputs, ChatGPT responses were significantly longer (331 vs. 218 words, P &lt; 0.001). Doximity GPT outputs were structured as a letter from a medical provider to the patient, whereas ChatGPT outputs were a bulleted list. Doximity GPT outputs were significantly more readable by four validated scales: Flesch Kincaid Reading Ease (42.6 vs. 29.9, P &lt; 0.001) and Flesch Kincaid Grade Level (11.4 vs. 14.1 grade, P &lt; 0.001), Coleman-Liau Index (14.9 vs. 17 grade, P &lt; 0.001), and Automated Readability Index (11.3 vs. 14.8 grade, P &lt; 0.001). Regarding content, there was no difference between the two platforms regarding the appropriateness of the topic (99% overall). Medical advice from all outputs was deemed reasonable. Conclusion: Doximity’s AI platform produces reasonable, accurate information in response to common patient queries. With continued reinforcement learning with human feedback (RLHF), Doximity GPT has the potential to be a useful tool to plastic surgeons and can assist with a range of tasks, such as providing basic information on procedures and writing appeal letters to insurance providers.

Management of Acute Coronary Syndrome in Elderly Patients: A Narrative Review through Decisional Crossroads.

Diagnosis and treatment of acute coronary syndrome (ACS) pose particular challenges in elderly patients. When high troponin levels are detected, the distinction between non-ischemic myocardial injury (NIMI), type 1, and type 2 myocardial infarction (MI) is the necessary first step to guide further care. However, the assessment of signs of ischemia is hindered in older patients, and no simple clinical or laboratory tool proved useful in this discrimination task. Current evidence suggests a benefit of an invasive vs. conservative approach in terms of recurrence of MI, with no significant impact on mortality. In patients with multivessel disease in which the culprit lesion has been treated, a physiology-guided complete percutaneous revascularization significantly reduced major events. The management of ACS in elderly patients is an example of the actual need for a multimodal, thorough clinical approach, coupled with shared decision-making, in order to ensure the best treatment and avoid futility. Such a need will likely grow throughout the next decades, with the aging of the world population. In this narrative review, we address pivotal yet common questions arising in clinical practice while caring for elderly patients with ACS.

Системи перерозподілення транспортних потоків. Транспортні розв'язки. Застосовувані терміни

Summary Introduction. In the introduction to the paper, the common questions concerning the assurance of the terminological congruence of national standards’ provisions, when being harmonized, with provisions of regional or international standards were outlined and also important sources of appropriate regional information in the sector of the transport infrastructure were identified. Problem statement. The issue of the paper concerns terminological matter in the domain of definitions for the interchange systems on motor roads peculiarly those constituting international traffic arteries and for elements of such systems. Purpose. The paper is aimed on the analysis of scientific sources and provisions of the EU regulatory acts to state the problems and develop the propositions concerning initial points for the enlargement of the structure of terminology relating the interchange systems on motor roads constituting international traffic arteries.

Introduction to Part 2 of the Themed Issue, ‘Racism and Colonialism in Hegel's Philosophy’: Common Objections and Questions for Future Research

In the first part of our editorial introduction to the themed issue ‘Racism and Colonialism in Hegel's Philosophy’ we outlined its rationale and some of its main topics. Here we address some common objections against research of this kind and formulate questions for further research.

Assessment of the Position of North Adriatic Terminals in Container Market Based on Different Indices

Background: This paper addresses the common question of how service providers compare to their competitors and what their competitive advantages are. It focuses on the North Adriatic ports of Trieste, Koper, Rijeka, Venice, and Ravenna, all members of the North Adriatic Ports Association, analyzing their position, size, competitiveness, and role in the container market. Methods: The research employs three distinct methods: market structure analysis using several concentration indices, the R method for ranking terminals, and aggregation of these indices to create a combined index for port performance. Results: Based on the rankings, indicators, the BCG matrix, and future development plans for each terminal, the ports are ranked as follows: Koper, Rijeka, Trieste, Venice, and Ravenna. Koper emerges as the leader, positioned in the high market share and strong growth category. This advantageous position allows Koper to efficiently attract traffic without requiring substantial investments. Conclusions: The contribution of the work is that the specific measure indices were applied, which were not used in the analysis of the North Adriatic container terminals until now. The research provides both a broad and detailed understanding of the position, role, and condition of each terminal. This insight enables stakeholders to take timely, strategic actions aimed at boosting productivity and traffic, ultimately improving their competitive standing.

Appropriateness and readability of Google Bard and ChatGPT-3.5 generated responses for surgical treatment of glaucoma.

To evaluate the appropriateness and readability of the medical knowledge provided by ChatGPT-3.5 and Google Bard, artificial-intelligence-powered conversational search engines, regarding surgical treatment for glaucoma. In this retrospective, cross-sectional study, 25 common questions related to the surgical management of glaucoma were asked on ChatGPT-3.5 and Google Bard. Glaucoma specialists graded the responses' appropriateness, and different scores assessed readability. Appropriate answers to the posed questions were obtained in 68% of the responses with Google Bard and 96% with ChatGPT-3.5. On average, the responses generated by Google Bard had a significantly lower proportion of sentences, having more than 30 and 20 syllables (23% and 52% respectively) compared to ChatGPT-3.5 (66% and 82% respectively), as noted by readability. Google Bard had significantly (p<0.0001) lower readability grade scores and significantly higher "Flesch Reading ease score", implying greater ease of readability amongst the answers generated by Google Bard. Many patients and their families turn to LLM chatbots for information, necessitating clear and accurate content. Assessments of online glaucoma information have shown variability in quality and readability, with institutional websites generally performing better than private ones. We found that ChatGPT-3.5, while precise, has lower readability than Google Bard, which is more accessible but less precise. For example, the Flesch Reading Ease Score was 57.6 for Google Bard and 22.6 for ChatGPT, indicating Google Bard's content is easier to read. Moreover, the Gunning Fog Index scores suggested that Google Bard's text is more suitable for a broader audience. ChatGPT's knowledge is limited to data up to 2021, whereas Google Bard, trained with real-time data, offers more current information. Further research is needed to evaluate these tools across various medical topics. The answers generated by ChatGPT-3.5™ AI are more accurate than the ones given by Google Bard. However, comprehension of ChatGPT-3.5™ answers may be difficult for the public with glaucoma. This study emphasized the importance of verifying the accuracy and clarity of online information that glaucoma patients rely on to make informed decisions about their ocular health. This is an exciting new area for patient education and health literacy.