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Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

BackgroundEmergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.ObjectiveEvaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED.DesignAn institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18–100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012–December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2–30 days post ED registration). A gradient boosting model was trained on data from years 2012–2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality.Key ResultsOverall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality.ConclusionThe gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

The field of gastroenterology (GI) is constantly evolving. It is essential to pinpoint the most pressing and important research questions. To evaluate the potential of chatGPT for identifying research priorities in GI and provide a starting point for further investigation. We queried chatGPT on four key topics in GI: inflammatory bowel disease, microbiome, Artificial Intelligence in GI, and advanced endoscopy in GI. A panel of experienced gastroenterologists separately reviewed and rated the generated research questions on a scale of 1–5, with 5 being the most important and relevant to current research in GI. chatGPT generated relevant and clear research questions. Yet, the questions were not considered original by the panel of gastroenterologists. On average, the questions were rated 3.6 ± 1.4, with inter-rater reliability ranging from 0.80 to 0.98 ( p  < 0.001). The mean grades for relevance, clarity, specificity, and originality were 4.9 ± 0.1, 4.6 ± 0.4, 3.1 ± 0.2, 1.5 ± 0.4, respectively. Our study suggests that Large Language Models (LLMs) may be a useful tool for identifying research priorities in the field of GI, but more work is needed to improve the novelty of the generated research questions.

The performance of artificial intelligence-aided colonoscopy (AIAC) in a real-world setting has not been described. We compared adenoma and polyp detection rates (ADR/PDR) in a 6-month period before (pre-AIAC) and after introduction of AIAC (GI Genius, Medtronic) in all endoscopy suites in our large-volume center. The ADR and PDR in the AIAC group was lower compared with those in the pre-AIAC group (30.3% vs 35.2%, P < 0.001; 36.5% vs 40.9%, P = 0.004, respectively); procedure time was significantly shorter in the AIAC group. In summary, introduction of AIAC did not result in performance improvement in our large-center cohort, raising important questions on AI-human interactions in medicine.

BackgroundIn the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic procedures.MethodsMedline, Embase, IEEE Xplore, and the Web of science databases were searched from January 2012 to May 5, 2020. Selected studies tested a deep learning model, specifically convolutional neural networks, for video analysis of laparoscopic surgery. Study characteristics including the dataset source, type of operation, number of videos, and prediction application were compared. A random effects model was used for estimating pooled sensitivity and specificity of the computer algorithms. Summary receiver operating characteristic curves were calculated by the bivariate model of Reitsma.ResultsThirty-two out of 508 studies identified met inclusion criteria. Applications included instrument recognition and detection (45%), phase recognition (20%), anatomy recognition and detection (15%), action recognition (13%), surgery time prediction (5%), and gauze recognition (3%). The most common tested procedures were cholecystectomy (51%) and gynecological—mainly hysterectomy and myomectomy (26%). A total of 3004 videos were analyzed. Publications in clinical journals increased in 2020 compared to bio-computational ones. Four studies provided enough data to construct 8 contingency tables, enabling calculation of test accuracy with a pooled sensitivity of 0.93 (95% CI 0.85–0.97) and specificity of 0.96 (95% CI 0.84–0.99). Yet, the majority of papers had a high risk of bias.ConclusionsDeep learning research holds potential in laparoscopic surgery, but is limited in methodologies. Clinicians may advance AI in surgery, specifically by offering standardized visual databases and reporting.

The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models’ consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT’s 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.

Background and aims: Patients frequently have concerns about their disease and find it challenging to obtain accurate Information. OpenAI’s ChatGPT chatbot (ChatGPT) is a new large language model developed to provide answers to a wide range of questions in various fields. Our aim is to evaluate the performance of ChatGPT in answering patients’ questions regarding gastrointestinal health. Methods: To evaluate the performance of ChatGPT in answering patients’ questions, we used a representative sample of 110 real-life questions. The answers provided by ChatGPT were rated in consensus by three experienced gastroenterologists. The accuracy, clarity, and efficacy of the answers provided by ChatGPT were assessed. Results: ChatGPT was able to provide accurate and clear answers to patients’ questions in some cases, but not in others. For questions about treatments, the average accuracy, clarity, and efficacy scores (1 to 5) were 3.9 ± 0.8, 3.9 ± 0.9, and 3.3 ± 0.9, respectively. For symptoms questions, the average accuracy, clarity, and efficacy scores were 3.4 ± 0.8, 3.7 ± 0.7, and 3.2 ± 0.7, respectively. For diagnostic test questions, the average accuracy, clarity, and efficacy scores were 3.7 ± 1.7, 3.7 ± 1.8, and 3.5 ± 1.7, respectively. Conclusions: While ChatGPT has potential as a source of information, further development is needed. The quality of information is contingent upon the quality of the online information provided. These findings may be useful for healthcare providers and patients alike in understanding the capabilities and limitations of ChatGPT.

This study explores the potential of OpenAI's ChatGPT as a decision support tool for acute ulcerative colitis presentations in the setting of an emergency department. We assessed ChatGPT's performance in determining disease severity using TrueLove and Witts criteria and the necessity of hospitalization for patients with ulcerative colitis, comparing results with those of expert gastroenterologists. Of 20 cases, ChatGPT's assessments were found to be 80% consistent with gastroenterologist evaluations and indicated a high degree of reliability. This suggests that ChatGPT could provide as a clinical decision support tool in assessing acute ulcerative colitis, serving as an adjunct to clinical judgment.

Background Writing multiple choice questions (MCQs) for the purpose of medical exams is challenging. It requires extensive medical knowledge, time and effort from medical educators. This systematic review focuses on the application of large language models (LLMs) in generating medical MCQs. Methods The authors searched for studies published up to November 2023. Search terms focused on LLMs generated MCQs for medical examinations. Non-English, out of year range and studies not focusing on AI generated multiple-choice questions were excluded. MEDLINE was used as a search database. Risk of bias was evaluated using a tailored QUADAS-2 tool. Results Overall, eight studies published between April 2023 and October 2023 were included. Six studies used Chat-GPT 3.5, while two employed GPT 4. Five studies showed that LLMs can produce competent questions valid for medical exams. Three studies used LLMs to write medical questions but did not evaluate the validity of the questions. One study conducted a comparative analysis of different models. One other study compared LLM-generated questions with those written by humans. All studies presented faulty questions that were deemed inappropriate for medical exams. Some questions required additional modifications in order to qualify. Conclusions LLMs can be used to write MCQs for medical examinations. However, their limitations cannot be ignored. Further study in this field is essential and more conclusive evidence is needed. Until then, LLMs may serve as a supplementary tool for writing medical examinations. 2 studies were at high risk of bias. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Background: The integration of artificial intelligence (AI) into healthcare has opened new avenues for enhancing patient care and clinical research. In gastroenterology, the potential of AI tools, specifically large language models like ChatGPT, is being explored to understand their utility and effectiveness. Objectives: The primary goal of this systematic review is to assess the various applications, ascertain the benefits, and identify the limitations of utilizing ChatGPT within the realm of gastroenterology. Design: Through a systematic approach, this review aggregates findings from multiple studies to evaluate the impact of ChatGPT on the field. Data sources and methods: The review was based on a detailed literature search of the PubMed database, targeting research that delves into the use of ChatGPT for gastroenterological purposes. It incorporated six selected studies, which were meticulously evaluated for quality using the Joanna Briggs Institute critical appraisal instruments. The data were then synthesized narratively to encapsulate the roles, advantages, and drawbacks of ChatGPT in gastroenterology. Results: The investigation unearthed various roles of ChatGPT, including its use in patient education, diagnostic self-assessment, disease management, and the formulation of research queries. Notable benefits were its capability to provide pertinent recommendations, enhance communication between patients and physicians, and prompt valuable research inquiries. Nonetheless, it encountered obstacles in decoding intricate medical questions, yielded inconsistent responses at times, and exhibited limitations in generating novel content. The review also considered ethical implications. Conclusion: ChatGPT has demonstrated significant potential in the field of gastroenterology, especially in facilitating patient–physician interactions and managing diseases. Despite these advancements, the review underscores the necessity for ongoing refinement, customization, and ethical regulation of AI tools. These findings serve to enrich the dialog concerning AI’s role in healthcare, with a specific focus on ChatGPT’s application in gastroenterology. Plain language summary Checking how ChatGPT works in gastroenterology: a detailed look at its uses, advantages, and challenges Goal We looked at how ChatGPT, a computer program, is used in the study Gastroenterology. We wanted to understand what’s good about it, what’s challenging, and how it can help doctors and patients. How We Did It We searched for articles about ChatGPT in Gastroenterology on PubMed. We found six suitable articles and checked their quality using the Joanna Briggs Institute (JBI) critical appraisal tools. Then, we put all the information together to get a clear picture. What We Found Doctors and researchers use ChatGPT in many ways. Some use it to teach patients about their health, while others use it to help patients check their symptoms or manage their conditions. It can even help come up with research questions. The good things about ChatGPT are that it gives helpful advice, makes talking between doctors and patients easier, and helps come up with research topics. But, sometimes it doesn’t understand hard medical questions, gives different answers for the same question, or lacks new ideas. There are also concerns about using it the right way. What This Means ChatGPT can be a helpful tool in Gastroenterology, especially when talking with patients and managing their health. But, there are challenges that need to be fixed. Our review helps people understand how ChatGPT can be used in health care, especially in the field of Gastroenterology.