Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Endocrine Entropy.

This communication defines and describes the novel concept of endocrine entropy. The authors share insights regarding the various facets of entropy in endocrine epidemiology, physiology, clinical presentation and management. The discussion opens up a new way of approaching endocrinology. Recent advances in artificial intelligence, assessment and addressal of entropy may become integral part of endocrine diagnostics and therapeutics.

Endocrine Entropy.

This communication defines and describes the novel concept of endocrine entropy. The authors share insights regarding the various facets of entropy in endocrine epidemiology, physiology, clinical presentation and management. The discussion opens up a new way of approaching endocrinology. Recent advances in artificial intelligence, assessment and addressal of entropy may become integral part of endocrine diagnostics and therapeutics.

Bioinformatics and machine learning to support nanomaterial grouping.

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

Evaluation of deep learning algorithms in detecting moyamoya disease: a systematic review and single-arm meta-analysis.

The diagnosis of Moyamoya disease (MMD) relies heavily on imaging, which could benefit from standardized machine learning tools. This study aims to evaluate the diagnostic efficacy of deep learning (DL) algorithms for MMD by analyzing sensitivity, specificity, and the area under the curve (AUC) compared to expert consensus. We conducted a systematic search of PubMed, Embase, and Web of Science for articles published from inception to February 2024. Eligible studies were required to report diagnostic accuracy metrics such as sensitivity, specificity, and AUC, excluding those not in English or using traditional machine learning methods. Seven studies were included, comprising a sample of 4,416 patients, of whom 1,358 had MMD. The pooled sensitivity for common and random effects models was 0.89 (95% CI: 0.85 to 0.92) and 0.92 (95% CI: 0.85 to 0.96), respectively. The pooled specificity was 0.89 (95% CI: 0.86 to 0.91) in the common effects model and 0.91 (95% CI: 0.75 to 0.97) in the random effects model. Two studies reported the AUC alongside their confidence intervals. A meta-analysis synthesizing these findings aggregated a mean AUC of 0.94 (95% CI: 0.92 to 0.96) for common effects and 0.89 (95% CI: 0.76 to 1.02) for random effects models. Deep learning models significantly enhance the diagnosis of MMD by efficiently extracting and identifying complex image patterns with high sensitivity and specificity. Trial registration: CRD42024524998 https://www.crd.york.ac.uk/prospero/displayrecord.php?RecordID=524998.

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.

The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.

Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.

Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.

Glottis Recognition Software Development Using Artificial Intelligence.

The use of video laryngoscopes has enhanced the visualization of the vocal cords, thereby improving the accessibility of tracheal intubation. Employing artificial intelligence (AI) to recognize images obtained through video laryngoscopy, particularly when marking the epiglottis and vocal cords, may elucidate anatomical structures and enhance anatomical comprehension of anatomy. This study investigates the ability of an AI model to accurately identify the glottis in video laryngoscope images captured from a manikin. Tracheal intubation was conducted on a manikin using a bronchoscope with recording capabilities, and image data of the glottis was gathered for creating an AI model. Data preprocessing and annotation of the vocal cords, epiglottis, and glottis were performed, and human annotation of the vocal cords, epiglottis, and glottis was carried out. Based on the AI's determinations, anatomical structures were color-coded for identification. The recognition accuracy of the epiglottis and vocal cords recognized by the AI model was 0.9516, which was over 95%. The AI successfully marked the glottis, epiglottis, and vocal cords during the tracheal intubation process. These markings significantly aided in the visual identification of the respective structures with an accuracy of more than 95%. The AI demonstrated the ability to recognize the epiglottis, vocal cords, and glottis using an image recognition model of a manikin.

Open optimism as an "embodied-health" ethic for the information era.

This article forms part of a series on "openness," "non-linearity," and "embodied-health" in the post-physical, informational (virtual) era of society. This is vital given that the threats posed by advances in artificial intelligence call for a holistic, embodied approach. Typically, health is separated into different categories, for example, (psycho)mental health, biological/bodily health, genetic health, environmental health, or reproductive health. However, this separation only serves to undermine health; there can be no separation of health into subgroups (psychosomatics, for example). Embodied health contains no false divisions and relies on "optimism" as the key framing value. Optimism is only achieved through the mechanism/enabling condition of openness. Openness is vital to secure the embodied health for individuals and societies. Optimism demands that persons become active participants within their own lives and are not mere blank slates, painted in the colors of physical determinism (thus a move away from nihilism-which is the annihilation of freedom/autonomy/quality). To build an account of embodied health, the following themes/aims are analyzed, built, and validated: (1) a modern re-interpretation and validation of German idealism (the crux of many legal-ethical systems) and Freud; (2) ascertaining the bounded rationality and conceptual semantics of openness (which underlies thermodynamics, psychosocial relations, individual autonomy, ethics, and as being a central constitutional governmental value for many regulatory systems); (3) the link between openness and societal/individual embodied health, freedom, and autonomy; (4) securing the role of individualism/subjectivity in constituting openness; (5) the vital role of nonlinear dynamics in securing optimism and embodied health; (6) validation of arguments using the methodological scientific value of invariance (generalization value) by drawing evidence from (i) information and computer sciences, (ii) quantum theory, and (iii) bio-genetic evolutionary evidence; and (7) a validation and promotion of the inalienable role of theoretic philosophy in constituting embodied health, and how modern society denigrates embodied health, by misconstruing and undermining theoretics. Thus, this paper provides and defends an up-to-date non-physical account of embodied health by creating a psycho-physical-biological-computational-philosophical construction. Thus, this paper also brings invaluable coherence to legal and ethical debates on points of technicality from the empirical sciences, demonstrating that each field is saying the same thing.

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.

Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.

Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum.

AIMS: The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum. METHODS: A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. RESULTS: We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. CONCLUSION: The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.

Masquerade of authority: hijacked journals are gaining more credibility than original ones.

At the moment, the academic world is faced with various challenges that negatively impact science integrity. One is hijacked journals, a second, inauthentic website for indexed legitimate journals, managed by cybercriminals. These journals publish any manuscript by charging authors and pose a risk to scientific integrity. This piece compares a journal's original and hijacked versions regarding authority in search engines. A list of 16 medical journals, along with their hijacked versions, has been collected. The MOZ Domain Authority has been used to check the authority of both original and hijacked journals, and the results have been discussed. It indicates that hijacked journals are gaining more credibility than original ones. This should alarm academia and highlights a need for serious action against hijacked journals. The related policies should be planned, and tools should be developed to support easy detection of hijacked journals. On the publishers' side, the visibility of journals' websites must be enhanced to address this issue.

Optimizing discharge antimicrobial therapy: Evaluation of a transitions of care process and electronic scoring system for patients with community-acquired pneumonia or chronic obstructive pulmonary disease.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: Prescribing excess antibiotic duration at hospital discharge is common. A pharmacist-led Antimicrobial Stewardship Program Transition of Care (ASP TOC) intervention was associated with improved discharge prescribing. To improve the sustainability of this service, an electronic scoring system (ESS), which included the ASP TOC electronic variable, was implemented in the electronic medical record to prioritize pharmacist workload. The purpose of this study was to evaluate the implementation of the ASP TOC variable in the ESS in patients with community-acquired pneumonia (CAP) or chronic obstructive pulmonary disease (COPD). METHODS: This institutional review board-approved, retrospective quasi-experiment included patients discharged on oral antibiotics for CAP or COPD exacerbation (lower respiratory tract infection) from November 1, 2021, to March 1, 2022 (the preintervention period) and November 1, 2022, to March 1, 2023 (the postintervention period). The primary endpoint was optimized discharge antimicrobial regimen. A sample of at least 194 patients was required to achieve 80% power to detect a 20% difference in the frequency of optimized therapy. Multivariable logistic regression was used to identify factors associated with optimized regimens. RESULTS: Similar baseline characteristics were observed in both study groups (n = 100 for both groups). The frequency of optimized discharge regimens improved from 69% to 82% (P = 0.033). The percentage of ASP TOC interventions documented as completed by a pharmacist increased from 4% to 25% (P < 0.001). ASP TOC intervention, female gender, and COPD were independently associated with an optimized discharge regimen (adjusted odds ratios, 6.57, 1.61, and 3.89, respectively; 95% CI, 1.51-28.63, 0.81-3.17, and 1.85-8.20, respectively). CONCLUSION: After the launch of the ASP TOC variable, there was an increase in optimized discharge regimens and ASP TOC interventions completed. Pharmacists' use of the ASP TOC variable through an ESS can aid in improving discharge prescribing.

Enhancing the Diagnostic Yield of Esophageal Manometry Using Distension Contraction Plots of Peristalsis & Artificial Intelligence.

BACKGROUND: Our prior study reveal that the distension-contraction profiles using high-resolution manometry impedance (HRMZ) recordings can distinguish patients with dysphagia symptom but normal esophageal function testing ("functional dysphagia") from controls. AIMS: To determine the diagnostic value of the recording protocol used in our prior studies (10cc swallows with subjects in the Trendelenburg position) against the standard clinical protocol (5cc swallows with subject in the supine position). We used advanced machine learning techniques and robust metrics for the classification purposes. METHODS: Studies were performed in 30 healthy subjects and 30 patients with functional dysphagia. A custom-built software was used to extract the relevant distension-contraction features of esophageal peristalsis. Ensemble methods, i.e., gradient boost, support vector machines (SVM), and logit boost were used as the primary machine learning algorithms. RESULTS: While the individual contraction features were marginally different between the two groups, the distension features of peristalsis were significantly different. The ROC curves values for the standard recording protocol, for the distension features ranged from 0.74 to 0.82; they were significantly better for the protocol used in our prior studies, ranged from 0.81-0.91. The ROC curve values using 3 machine learning algorithms were far superior for the distension than the contraction features of esophageal peristalsis, revealing value of 0.95 for the SVM algorithm. CONCLUSIONS: Current patient classification based on the contraction phase of peristalsis misses large number of patients who have abnormality in the distension phase of peristalsis. Distension contraction plots should be the standard of assessing esophageal peristalsis in clinical practice.

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its application to pediatric populations remains underexplored. In this study, we trained a convolutional neural network (AI-pECG) on paired ECG-echocardiograms (≤ 2 days apart) to detect ASD2 from patients ≤ 18 years old without major congenital heart disease. Model performance was evaluated on the first ECG-echocardiogram pair per patient for Boston Children's Hospital internal testing and emergency department cohorts using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves. The training cohort comprised of 92,377 ECG-echocardiogram pairs (46,261 patients; median age 8.2 years) with an ASD2 prevalence of 6.7%. Test groups included internal testing (12,631 patients; median age 7.4 years; 6.9% prevalence) and emergency department (2,830 patients; median age 7.5 years; 4.9% prevalence) cohorts. Model performance was higher in the internal test (AUROC 0.84, AUPRC 0.46) cohort than the emergency department cohort (AUROC 0.80, AUPRC 0.30). In both cohorts, AI-pECG outperformed ECG findings of incomplete right bundle branch block. Model explainability analyses suggest high-risk limb lead features include greater amplitude P waves (suggestive of right atrial enlargement) and V1 RSR' (suggestive of RBBB). Our findings demonstrate the promise of AI-pECG to inexpensively screen and/or detect ASD2 in pediatric patients. Future multicenter validation and prospective trials to inform clinical decision making are warranted.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).

BACKGROUND: Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos. METHODS: To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution. RESULTS: After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively. CONCLUSIONS: AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.

Diagnostic performances of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro in "Diagnosis Please" cases.

PURPOSE: Large language models (LLMs) are rapidly advancing and demonstrating high performance in understanding textual information, suggesting potential applications in interpreting patient histories and documented imaging findings. As LLMs continue to improve, their diagnostic abilities are expected to be enhanced further. However, there is a lack of comprehensive comparisons between LLMs from different manufacturers. In this study, we aimed to test the diagnostic performance of the three latest major LLMs (GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro) using Radiology Diagnosis Please Cases, a monthly diagnostic quiz series for radiology experts. MATERIALS AND METHODS: Clinical history and imaging findings, provided textually by the case submitters, were extracted from 324 quiz questions originating from Radiology Diagnosis Please cases published between 1998 and 2023. The top three differential diagnoses were generated by GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro, using their respective application programming interfaces. A comparative analysis of diagnostic performance among these three LLMs was conducted using Cochrane's Q and post hoc McNemar's tests. RESULTS: The respective diagnostic accuracies of GPT-4o, Claude 3 Opus, and Gemini 1.5 Pro for primary diagnosis were 41.0%, 54.0%, and 33.9%, which further improved to 49.4%, 62.0%, and 41.0%, when considering the accuracy of any of the top three differential diagnoses. Significant differences in the diagnostic performance were observed among all pairs of models. CONCLUSION: Claude 3 Opus outperformed GPT-4o and Gemini 1.5 Pro in solving radiology quiz cases. These models appear capable of assisting radiologists when supplied with accurate evaluations and worded descriptions of imaging findings.

Artificial Intelligence Application in Skull Bone Fracture with Segmentation Approach.

This study aims to evaluate an AI model designed to automatically classify skull fractures and visualize segmentation on emergent CT scans. The model's goal is to boost diagnostic accuracy, alleviate radiologists' workload, and hasten diagnosis, thereby enhancing patient outcomes. Unique to this research, both pediatric and post-operative patients were not excluded, and diagnostic durations were analyzed. Our testing dataset for the observer studies involved 671 patients, with a mean age of 58.88 years and fairly balanced gender representation. Model 1 of our AI algorithm, trained with 1499 fracture-positive cases, showed a sensitivity of 0.94 and specificity of 0.87, with a DICE score of 0.65. Implementing post-processing rules (specifically Rule B) improved the model's performance, resulting in a sensitivity of 0.94, specificity of 0.99, and a DICE score of 0.63. AI-assisted diagnosis resulted in significantly enhanced performance for all participants, with sensitivity almost doubling for junior radiology residents and other specialists. Additionally, diagnostic durations were significantly reduced (p < 0.01) with AI assistance across all participant categories. Our skull fracture detection model, employing a segmentation approach, demonstrated high performance, enhancing diagnostic accuracy and efficiency for radiologists and clinical physicians. This underlines the potential of AI integration in medical imaging analysis to improve patient care.

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.

PURPOSE OF REVIEW: Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management. RECENT FINDINGS: We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.