Artificial Intelligence Software Research Articles

Irrigation with Artificial Intelligence: Problems, Premises, Promises

AbstractProtagonists allege that artificial intelligence (AI) is revolutionising contemporaneous mindscapes. Here, we authoritatively review the status quo of AI and machine learning application in irrigated agriculture, evaluating the potential of, and challenges associated with, a wide range of existential AI approaches. We contend that aspiring developers of AI irrigation systems may benefit from human-centred AI, a nascent algorithm that captures diverse end-user views, behaviours and actions, potentially facilitating refinement of proposed systems through iterative stakeholder feedback. AI-guided human–machine collaboration can streamline integration of user needs, allowing customisation towards situational farm management adaptation. Presentation of big data in intuitive, legible and actionable forms for specialists and laypeople also urgently requires attention: here, AI-explainable interpretability may help harness human expertise, enabling end-users to contribute their experience within an AI pipeline for bespoke outputs. Transfer learning holds promise in contextualising place-based AI to agroecological regions, production systems or enterprise mixes, even with limited data inputs. We find that the rate of AI scientific and software development in recent times has outpaced the evolution of adequate legal and institutional regulations, and often social, moral and ethical license to operate, revealing consumer issues associated with data ownership, legitimacy and trust. We opine that AI has great potential to elicit sustainable outcomes in food security, social innovation and environmental stewardship, albeit such potential is more likely to be realised through concurrent development of appropriate ethical, moral and legal dimensions.

Using Generative AI Midjourney to Enhance Divergent and Convergent Thinking in an Architect’s Creative Design Process

Architects use a range of tools, from the traditional pencil to Virtual Reality technologies to prototype and articulate their creative designs. In recent years, Generative Artificial Intelligence (GenAI) software has reached the mainstream and there is an exponential appearance of GenAI images that portray architectural designs. This article documents an architectural design methodology that uses Midjourney, a text-to-image GenAI software, as a design tool that enhances architects’ creativity. Prompted by a design brief, we used Midjourney to accelerate (1) the identification and refinement process of developing a prospective user and (2) the ideation process of creating different desirable spaces for the designed user.

Use of Artificial Intelligence With Deep Learning Approaches for the Follow-up of Infrarenal Endovascular Aortic Repair.

Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events. Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions. Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm3 [IQR: 113.8-171.7] vs 125.4 cm3 [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711). The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter. The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair.

Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy.

Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI's diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI's sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI's sensitivity was 77.78% with an F1 score of 84.00%. The AI's specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

Artificial intelligence-based differential diagnosis of orbital MALT lymphoma and IgG4 related ophthalmic disease using hematoxylin-eosin images.

To investigate the possibility of distinguishing between IgG4-related ophthalmic disease (IgG4-ROD) and orbital MALT lymphoma using artificial intelligence (AI) and hematoxylin-eosin (HE) images. After identifying a total of 127 patients from whom we were able to procure tissue blocks with IgG4-ROD and orbital MALT lymphoma, we performed histological and molecular genetic analyses, such as gene rearrangement. Subsequently, pathological HE images were collected from these patients followed by the cutting out of 10 different image patches from the HE image of each patient. A total of 970 image patches from the 97 patients were used to construct nine different models of deep learning, and the 300 image patches from the remaining 30 patients were used to evaluate the diagnostic performance of the models. Area under the curve (AUC) and accuracy (ACC) were used for the performance evaluation of the deep learning models. In addition, four ophthalmologists performed the binary classification between IgG4-ROD and orbital MALT lymphoma. EVA, which is a vision-centric foundation model to explore the limits of visual representation, was the best deep learning model among the nine models. The results of EVA were ACC = 73.3% and AUC = 0.807. The ACC of the four ophthalmologists ranged from 40 to 60%. It was possible to construct an AI software based on deep learning that was able to distinguish between IgG4-ROD and orbital MALT. This AI model may be useful as an initial screening tool to direct further ancillary investigations.

Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance

ObjectiveIn physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers.MethodsFemale patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves.ResultsA total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists’ group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer.ConclusionsThe AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.

Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers

We aimed to develop a new artificial intelligence software that can automatically extract and measure the volume of white matter hyperintensities (WMHs) in head magnetic resonance imaging (MRI) using only thick-slice fluid-attenuated inversion recovery (FLAIR) sequences from multiple centers. We enrolled 1092 participants in Japan, comprising the thick-slice Private Dataset. Based on 207 randomly selected participants, neuroradiologists annotated WMHs using predefined guidelines. The annotated images of participants were divided into training (n = 138) and test (n = 69) datasets. The WMH segmentation model comprised a U-Net ensemble and was trained using the Private Dataset. Two other models were trained for validation using either both thin- and thick-slice MRI datasets or the thin-slice dataset alone. The voxel-wise Dice similarity coefficient (DSC) was used as the evaluation metric. The model trained using only thick-slice MRI showed a DSC of 0.820 for the test dataset, which is comparable to the accuracy of human readers. The model trained with the additional thin-slice dataset showed only a slightly improved DSC of 0.822. This automatic WMH segmentation model comprising a U-Net ensemble trained on a thick-slice FLAIR MRI dataset is a promising new method. Despite some limitations, this model may be applicable in clinical practice.

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Agreement of ejection fraction measured by coronary computed tomography (CT) and cardiac ultrasound in evaluating patients with chronic heart failure: an observational comparative study.

Cardiac ultrasound is one of the most important examinations in cardiovascular medicine, but the technical requirements for the operator are relatively high, which to some extent affects the scope of its use. This study was dedicated to investigating the agreement of ejection fraction between coronary computed tomography (CT) and cardiac ultrasound and diagnostic performance in evaluating the clinical diagnosis of patients with chronic heart failure. We conducted a single-center-based retrospective study including 343 consecutive patients enrolled between January 2019 to April 2020, all of whom presented with suspected symptoms of heart failure within one month. All enrolled cases performed cardiac ultrasound and coronary CT scans. The CT images were analyzed using accurate left ventricle (AccuLV) artificial intelligence (AI) software to calculate the ejection fraction-computed tomography (EF-CT) and it was compared with the ejection fraction (EF) obtained based on ultrasound. Cardiac insufficiency was determined if the EF measured by ultrasound was below 50%. Diagnostic performance analysis, correlation analysis and Bland-Altman plot were used to compare agreement between EF-CT and CT. Of the 319 successfully performed patients, 220 (69%) were identified as cardiac insufficiency. Quantitative consistency analysis showed a good correlation between EF-CT and EF values in all cases (R square =0.704, r=0.837). Bland-Altman analysis showed mean bias of 6.6%, mean percentage error of 27.5% and 95% limit of agreement of -17% to 30% between EF and EF-CT. The results of the qualitative diagnostic study showed that the sensitivity and specificity of EF measured by coronary CT reached a high level of 91% [95% confidence interval (CI): 86-94%], and the positive diagnostic value was up to 96% (95% CI: 92-98%). The EF-CT and EF have excellent agreement, and AccuLV-based AI left ventricular function analysis software perhaps can be used as a clinical diagnostic reference.

Shorter Time to Assessment and Anticoagulation with Decreased Mortality in Patients with Pulmonary Embolism Following Implementation of Artificial Intelligence Software

Shorter Time to Assessment and Anticoagulation with Decreased Mortality in Patients with Pulmonary Embolism Following Implementation of Artificial Intelligence Software

Use of Artificial Intelligence Software (Viz-AI) to Detect Intracranial Aneurysms: A Comprehensive Stroke Center Experience.

Use of Artificial Intelligence Software (Viz-AI) to Detect Intracranial Aneurysms: A Comprehensive Stroke Center Experience.

Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy.

Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.

[Exploration and practice of cardio-oncology].

With the improvement of oncology diagnosis and treatment, the survival time of cancer patients has been significantly prolonged, and the cancer therapy-related cardiovascular toxicity such as radiotherapy, chemotherapy, immunotherapy, and surgery are becoming more and more prominent, and it is in this context that the germ of Cardio-Oncology exploration has come into being. The multidisciplinary Cardio-Oncology team aims to establish a multidisciplinary prevention and control system to assess patients' baseline risk factors, individualized monitoring, and weighing the risk-benefit ratio of cancer therapy. At present, the connotation of the discipline of Cardio-Oncology has been expanded horizontally and deepened vertically in China, and Cardio-Oncology treatment centers have blossomed all over the country. Moreover, international and domestic scholars continue to improve Cardio-Oncology guidelines and consensus through their own practice, and develop artificial intelligence software to help the development of the discipline. It is believed that in the future, with the training of Cardio-Oncologists and the output of high-quality clinical research evidence, cardiovascular safety of cancer patients can be ensured more scientifically and effectively.

Clinical evaluation of the efficacy of limbus artificial intelligence software to augment contouring for prostate and nodes radiotherapy

Abstract Objectives To determine if Limbus, an artificial intelligence (AI) auto-contouring software, can offer meaningful time savings for prostate radiotherapy treatment planning. Methods Three clinical oncologists recorded the time taken to contour prostate and seminal vesicles, lymph nodes, bladder, rectum, bowel, and femoral heads on CT scans for 30 prostate patients (15 prostate, 15 prostate and nodes). Limbus 1.6.0 was used to generate these contours on the 30 CT scans. The time taken by the oncologists to modify individual Limbus contours was noted and compared with manual contouring times. The geometric similarity of Limbus and expert contours was assessed using the Dice Similarity Coefficient (DSC), and the dosimetric impact of using un-edited Limbus organs at risk contours was studied. Results Limbus reduced the time to produce clinically acceptable contours by 26 minutes for prostate and nodes patients and by 13 minutes for the prostate only patients. DSC values of greater than 0.7 were calculated for all contours, demonstrating good initial agreement. A dosimetric study showed that 5 of the 20 plans optimized using unmodified AI structures required unnecessary compromise of PTV coverage, highlighting the importance of expert review. Conclusions Limbus offers significant time saving and has become an essential part of our clinical practice. Advances in knowledge This article is the first to include bowel and lymph nodes when assessing potential time savings using Limbus software. It demonstrates that Limbus can be used as an aid for prostate and node radiotherapy treatment planning.

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.

To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

The Application of Large Language Models Reducing Cultural Barriers in International Trade: A Perspective from Cultural Conflicts, Potential and Obstacles

Large Language Models (LLMs) are powerful Artificial General Intelligence (AGI) software that generate natural language texts on various topics and domains. They have great potential to reduce cultural barriers in international trade by facilitating machine translation and cross-cultural communication. However, they also face cultural conflicts, challenges and risks, such as cultural misreading, bias, and ethical issues. This paper provides a comprehensive analysis of the current state and future prospects of LLMs in international trade, and discusses the cultural differences, opportunities and obstacles that they encounter. It also proposes some possible solutions and directions for improving the cultural sensitivity and performance of LLMs, such as incorporating cultural knowledge, enhancing cultural awareness, and ensuring cultural diversity and inclusivity. To illustrate the practical implications of LLMs in international trade, this paper provides some examples of how LLMs have been used or could be used in various trade scenarios, such as ChatGPT,NewBing and Ernie Bot. This paper aims to offer some insights and suggestions for researchers, practitioners, and policymakers who are interested in or involved in the development and application of LLMs in international trade, and to demonstrate how LLMs can contribute to the advancement and innovation of international trade.

Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases.

The aim was to compare ophthalmic diagnoses made by veterinarians to a deep learning (artificial intelligence) software tool which was developed to aid in the diagnosis of equine ophthalmic diseases. As equine ophthalmology is a very specialised field in equine medicine, the tool may be able to help in diagnosing equine ophthalmic emergencies such as uveitis. In silico tool development and assessment of diagnostic performance. A deep learning tool which was developed and trained for classification of equine ophthalmic diseases was tested with 40 photographs displaying various equine ophthalmic diseases. The same data set was shown to different groups of veterinarians (equine, small animal, mixed practice, other) using an opinion poll to compare the results and evaluate the performance of the programme. Convolutional Neural Networks (CNN) were trained on 2346 photographs of equine eyes, which were augmented to 9384 images. Two hundred and sixty-one separate unmodified images were used to evaluate the trained network. The trained deep learning tool was used on 40 photographs of equine eyes (10 healthy, 12 uveitis, 18 other diseases). An opinion poll was used to evaluate the diagnostic performance of 148 veterinarians in comparison to the software tool. The probability for the correct answer was 93% for the AI programme. Equine veterinarians answered correctly in 76%, whereas other veterinarians reached 67% probability for the correct diagnosis. Diagnosis was solely based on images of equine eyes without the possibility to evaluate the inner eye. The deep learning tool proved to be at least equivalent to veterinarians in assessing ophthalmic diseases in photographs. We therefore conclude that the software tool may be useful in detecting potential emergency cases. In this context, blindness in horses may be prevented as the horse can receive accurate treatment or can be sent to an equine hospital. Furthermore, the tool gives less experienced veterinarians the opportunity to differentiate between uveitis and other ocular anterior segment disease and to support them in their decision-making regarding treatment.

An Artificial Intelligence–Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum

ObjectiveThis study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. MethodsAI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. ResultsThere was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. ConclusionThis is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.

Does ChatGPT Answer Otolaryngology Questions Accurately?

Investigate the accuracy of ChatGPT in the manner of medical questions related to otolaryngology. A ChatGPT session was opened within which 93 questions were asked related to otolaryngology topics. Questions were drawn from all major domains within otolaryngology and based upon key action statements (KAS) from clinical practice guidelines (CPGs). Twenty-one "patient-level" questions were also asked of the program. Answers were graded as either "correct," "partially correct," "incorrect," or "non-answer." Correct answers were given at a rate of 45.5% (71.4% correct in patient-level, 37.3% CPG); partially correct answers at 31.8% (28.6% patient-level, 32.8% CPG); incorrect at 21.6% (0% patient-level, 28.4% CPG); and 1.1% non-answers (% patient-level, 1.5% CPG). There was no difference in the rate of correct answers between CPGs published before or after the period of data collection cited by ChatGPT. CPG-based questions were less likely to be correct than patient-level questions (p = 0.003). Publicly available artificial intelligence software has become increasingly popular with consumers for everything from story-telling to data collection. In this study, we examined the accuracy of ChatGPT responses to questions related to otolaryngology over 7 domains and 21 published CPGs. Physicians and patients should understand the limitations of this software as it applies to otolaryngology, and programmers in future iterations should consider giving greater weight to information published by well-established journals and written by national content experts. N/A Laryngoscope, 2024.

Early Experience With Artificial Intelligence Software to Detect Intracranial Occlusive Stroke in Trauma Patients.

Objective Identifying ischemic stroke is a diagnostic challenge in the trauma subpopulation. We describe our early experience with artificial intelligence-assisted image analysis software for automatically identifying acute ischemic stroke in trauma patients. Methods Patients were retrospectively screened for (i) admission to the trauma service at a level one trauma center between 2020 and 2022, (ii) radiologist-confirmed intracranial occlusion, (iii) occlusion identified on computed tomography angiography performed within 24 hours of admission, (iv) no intracranial hemorrhage, and (v) contemporaneous analysis with the large vessel occlusion (LVO) detection program. Baseline characteristics, stroke detection, response-activation, and outcome data were summarized. Results Of 9893 trauma patients admitted, 88 (0.89%) patients had a cerebral stroke diagnosis, of which 10 patients (10/88; 11.4%) met inclusion criteria. Most patients were admitted following a fall (8/10; 80%). Six (6/10; 60.0%) patients had LVOs. The program correctly detected 83.3% (5/6) of patients, and these patients were triaged in less than one hour from arrival on average. The program did not falsely identify non-LVOs as LVOs for any patients. Conclusions Identifying adjunct tools to aid timely identification and treatment of ischemic stroke in trauma patients is necessary to increase the chances for meaningful neurological recovery. Our early experience exhibited potential for using automated software to aid occlusion identification and subsequent stroke team mobilization. Future studies in larger cohorts will expand upon these preliminary findings to establish the accuracy and clinical benefit of automated stroke detection tool integration for the trauma population.