AI Solution Research Articles

DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep learning techniques.

Signaling dynamics encode important features and regulatory mechanisms of biological systems, and recent studies have reported the use of simulated signaling dynamics with mechanistic modeling as biomarkers for human diseases. Given the success of deep learning techniques, it is expected that they can extract informative patterns from simulation results more effectively than traditional approaches involving manual feature selection, which can be used for subsequent analyses, such as patient stratification and survival prediction. Here, we propose DynProfiler, which utilizes the entire signaling dynamics, including intermediate variables, as input and leverages deep learning techniques to extract informative features without requiring any labels. Furthermore, DynProfiler incorporates a modern explainable AI solution to provide quantitative time-dependent importance scores for each dynamics. Using simulated dynamics of patients with breast cancer as an example, we demonstrate DynProfiler's ability to extract high-quality features that can predict mortality risk and identify important dynamics, highlighting upregulated phosphorylated GSK3β as a biomarker for poor prognosis. Overall, this tool can be useful for clinical application, as well as for elucidating biological system dynamics. The DynProfiler Python library is available in GitHub at https://github.com/okadalabipr/DynProfiler.

Codesigning a Clinical Prediction Model for Aboriginal Perinatal Mental Health Using Glassbox AI and Aboriginal Wisdom and Lived Experience.

Perinatal mental health is vital for mothers, infants, and families. Aboriginal mothers, despite their strengths, face greater mental health disparities due to colonisation and trauma. Traditional screening methods lack cultural sensitivity. The Baby Coming You Ready (BCYR) program offers a culturally sensitive assessment with promising pilot results. To support health professionals, an AI solution using the Explainable Boosting Machine (EBM) is proposed, trained with Aboriginal lived experiences. This model identifies maternal protective and risk factors, offering interpretable predictions for holistic culturally sensitive care.

A multicentre study to evaluate the diagnostic performance of a novel CAD software, DecXpert, for radiological diagnosis of tuberculosis in the northern Indian population

Tuberculosis (TB) is the leading cause of mortality among infectious diseases globally. Effectively managing TB requires early identification of individuals with TB disease. Resource-constrained settings often lack skilled professionals for interpreting chest X-rays (CXRs) used in TB diagnosis. To address this challenge, we developed “DecXpert” a novel Computer-Aided Detection (CAD) software solution based on deep neural networks for early TB diagnosis from CXRs, aiming to detect subtle abnormalities that may be overlooked by human interpretation alone. This study was conducted on the largest cohort size to date, where the performance of a CAD software (DecXpert version 1.4) was validated against the gold standard molecular diagnostic technique, GeneXpert MTB/RIF, analyzing data from 4363 individuals across 12 primary health care centers and one tertiary hospital in North India. DecXpert demonstrated 88% sensitivity (95% CI 0.85–0.93) and 85% specificity (95% CI 0.82–0.91) for active TB detection. Incorporating demographics, DecXpert achieved an area under the curve of 0.91 (95% CI 0.88–0.94), indicating robust diagnostic performance. Our findings establish DecXpert's potential as an accurate, efficient AI solution for early identification of active TB cases. Deployed as a screening tool in resource-limited settings, DecXpert could enable early identification of individuals with TB disease and facilitate effective TB management where skilled radiological interpretation is limited.

IDMap: Leveraging AI and Data Technologies for Early Cancer Detection

Cancer screening is vital in cutting mortality rates, and containing the impact of cancer in a worldwide basis. The current conventional detection techniques including imaging and biopsy though efficient are also characterized with drawbacks like; invasive, expensive, and inaccurate. This abstract will describe the new AI and data solution in the fight against early cancer detection, which presents a massive opportunity to improve accuracy, cut down the time that it takes to deliver a diagnosis, and bring quality health care to possibly millions of patients. ML and, in particular, DL are prospective in terms of decision making upon medical data including imaging, genomic sequences, and electronic health records to detect biomarkers of cancer in early stages. The statistics show that the AI-driven systems are capable to provide better diagnostic outcomes than conventional methods in some fields including mammography for breast cancer and CT for lung. Moreover, AI’s integration in genomic studies helps in determining Cancer related genes and biomarkers hence supporting precision medicine that adapts treatment to the specific genetic information of the patient. Apart from having outlets in AI, big data analytics, cloud computing, and IoT are equally important in early cancer detection as well. Big data analysis enables the analysis of large and complicated data sets with the aid of which one may identify inklings that may point towards possible early development of cancer. The use of cloud computing in health care mainly provides meaningful platforms for the storage and management of the large volumes of medical data in a way that allows improved efficiency and high levels of security. Wearable sensors collect data on different biomarkers throughout a patient’s body, and convey real-time information regarding whether the biomarkers’ levels are approaching cancerous state. Despite this great promise, there are various issues that have to be solved: data protection, privacy, and security, problems with the algorithms’ biases, and integration into practice. Ethical questions are generally important to tackle the uncertainty surrounding data and decision-making in clinical care using A I systems. The future trends in early cancer diagnostics will involve deeper integration of the approaches as AI and big data technology, which will enable more precise prevention and treatment. The applicability of this approach can also extend to the early identification of cancer, but also the prevention of its occurrence through proper intervention. In conclusion, conversing AI and data technologies will be useful for enhancing the efficiency of the early cancer diagnosis, and that is why the perspectives for the patients’ recovery and the further decrease in the mortality rates connected with cancer are rather promising. The approaches in this area remain informative developing technologies that are likely to be integrated into clinical work as the leading organizational models for the future of oncology and preventive health.

Geospatial AI solution to monitor and mitigate increasing adverse ecological and hydrological impacts of climate change in Uttarakhand Himalaya (India).

Though climate change and its adverse ecological and geohydrological impacts are being experienced across the world in all types of ecosystems but as far as the Himalaya mountain ecosystem is concerned, the rate of climate change and subsequent impacts have reached an alarming stage due to anthropogenic and technogenic intervention on natural process and now need most effective and less time taking management strategy. Addressing this burning environmental problem, a geospatial artificial intelligence (GeoAI) technique-based case study is presented here from one of the most densely populated and urbanized regions of Himalaya mountain, viz Uttarakhand Himalaya, which is also called central Himalaya. The results of the study suggest that due to quite a high rate of climate change, the climatic zones shifting towards higher altitudes at the average rate of 5.6 2m/year, causing several adverse ecological impacts in terms of decreasing quality dense temperate forest cover (0.05%/year), snow cover (0.02%/year), water bodies (0.01%/year), agricultural land (0.31%/year), and horticultural land (0.01%/year). Conversion of these eco-friendly land use land cover into barren land, fallow land, and built-up land causes geohydrological consequences of climate change in terms of decreasing rainy days (1%/year), drying perennial springs (0.20%/year), perennial streams (0.11%/year), decreasing spring and stream discharge during non-monsoon season, increased extreme rainfall events (6-8%/year), and subsequent surface runoff during monsoon season. Further, the study advocates that the degraded geohydrological process has resulted in an increased frequency of disaster events (floods, cloudbursts, landslides. etc.) with a 3% (12 events) annual rate, causing great loss of environment, infrastructure, lives, and economy each year. Therefore, it has been very urgent to mitigate climate change and increase geohydrological disaster events through an integrated approach. Keep in view this, the present study proposed an integrated watershed management plan which is equally useful to be implemented across the Himalaya region and other similar ecosystems across the world.

Creating a "Ready-to-Use" AI Agent for Navigating Digital Platform to Enhance Collaborative Efficiency

The study aims to address the prevalent issue of navigation inefficiency within digital collaboration platforms, a critical factor affecting user productivity and satisfaction. This paper identifies research gaps and highlights the potential of multimodal AI Agents in revolutionising user experience in these platforms. Specifically, the development of a ready-to-use AI Agent was designed for the DingTalk platform, capable of guiding users through various digital functionalities via conversational interfaces. By leveraging recent advancements in large language models (LLMs) and the concept of "Model as a Service" (MaaS), the proposed AI solution seeks to overcome current navigation obstacles, thereby enhancing the sustainability and effectiveness of digital work ecosystems.

Tolerance of plantain (Plantago lanceolata) to translocated herbicides wiped on flower stems

ABSTRACT Growing plantain (Plantago lanceolata) within pastures reduces nitrate leaching following grazing. Plantain is usually grown with grass and clover species, making selective weed control challenging. One strategy to selectively remove tall weeds in pastures with mixed species is to apply herbicides using weed wipers. However, for many months of the year, plantain has upright leafless flower stems that will intercept herbicide applied using wipers. To determine if this means plantain might be damaged by wipers used in such swards, two glasshouse experiments tested the tolerance of mature plantain plants to the application of herbicides suitable for use in weed wipers (glyphosate, clopyralid, aminopyralid, dicamba, metsulfuron, picloram and triclopyr) to the flower stems. Aminopyralid (0.075% ai solution) and a low rate of glyphosate (1.8% ai) caused the least decrease in plantain biomass and should be safe for weed wiping within swards containing plantain. A higher rate of glyphosate (7.2% ai) and a glyphosate/metsulfuron mix (0.9% + 0.01% ai, respectively) caused the most damage to plantain, particularly following simulated rainfall after application. Thus, applying some herbicides using wipers should be possible for selective weed control within plantain swards even if rain falls soon afterwards, though field trials are required to fine-tune recommendations.

P-047 Sperm moving pattern analysis with artificial intelligence solution predicts aneuploidy ratios of zygotes

Abstract Study question Can an analysis of sperm movements with artificial intelligence predict aneuploidy ratios of zygotes? Summary answer And sperm movement patterns analyzed by AI diagnosis predicted the aneuploidy ratios of zygotes. What is known already Sperm motilities present a specific pattern like strength, strength with curving, curving only and none progressive et al. Sperm movement patterns are determined by the sperm neck protein which is the centromere protein. Also, this centromere protein of the sperms was involved in the chromosome segregation in the zygote. Study design, size, duration A total of 8,000 clinical participants were recruited by CHA Infertility Center Seoul Station and 5 videos were taken from the semen sample of each participant. Machine learning with sperm moving clip for the diagnosis algorithm predicted aneuploidy ratios of fertilized embryos. Participants/materials, setting, methods We used YOLO_v8 and EfficientNet algorithm on the extracted sperm movement trajectory image to classify sperm into classes according to their motion pattern. This information was combined with the age of the patient to predict the possibility of infertility by analyzing the association with aneuploidy ratios of zygotes using deep learning model. Main results and the role of chance Deep learning AI algorithm utilized diagnosis for unexplained male infertility. No strength moving sperm pattern has shown association with male origin aneuploidy after fertilization. Especially high ratios of cycle moving sperm significantly increased the aneuploidy ratios in the zygote. But paternal aging has no correlation with aneuploidy ratios in the embryo. AI diagnosis have a 0.9 accuracy score. It is expected that the AI solution generated through this system can help overcome the problem of finding the cause of male infertility by predicting aneuploidy ratios of zygotes. Limitations, reasons for caution This study is designed for and performed with a minimum number of sperm move data. For clinical application, further mass data was set-up to increase accuracy of diagnosis. Wider implications of the findings Our AI solution was constructed to predict the possibility of aneuploidy ratios in the zygote by analyzing and classifying the movement patterns of sperm. Therefore, sperm movement-based AI diagnosis solution can be applied to define unexplained male infertility. Trial registration number non-clinical trials

Effect of Stocking Density during CO2 Stunning of Pigs on Induction Time and Activity Level Measured Using AI.

During the CO2 stunning of pigs, a variation in their reaction to the gas and the duration of the induction period is observed. The stunning process can be affected by several conditions, such as stressful events and previous experiences, but the stocking density in the gondola may also have an impact. The objective was to investigate the effect of stocking density on the stunning process under commercial conditions. To quantify the pigs' reactions under industrial settings with a stocking density of up to eight pigs pr. Gondola (3.91 m2), the activity level was measured using an AI solution. Compared with a simulation of the expected induction period, a significantly longer induction period was found in gondolas containing seven and eight pigs (p < 0.001) but not when the gondolas contained three or four pigs. Both high and mean activity levels were significantly higher when stocking density was increased from three or four pigs to seven or eight pigs. The stunning process was thus negatively affected when increasing the stocking density. More knowledge is needed to explain this effect and to make statements on optimal stocking density. The measured activity levels may be a useful tool for obtaining information under commercial conditions and for documenting animal welfare.

Innovative AI Solution for Diabetic Retinopathy Health

Innovative AI Solution for Diabetic Retinopathy Health

Multicenter study evaluating the utility of AI-aided HER2 scoring in the clinically relevant categories.

e15150 Background: The availability of multiple therapeutic options for breast cancer patients with varying levels of protein expression necessitates precise and standardized HER2 scoring. This study explored the clinical utility of AI-aided HER2 scoring of whole-slide digital images by pathologists in breast core needle biopsies and excisions. Methods: The study included patients (n=764) with multiple subtypes of invasive breast cancer from 3 US reference laboratories and 1 academic medical center. The non-interventional two-arm multi-reader study retrospectively compared manual digital vs. AI-aided HER2 scoring of 8 pathologists. The fully automated HER2 AI solution detects the invasive tumor area and then classifies the tumor cells based on staining pattern based and the 2023 ASCO/CAP guidelines to output slide-level HER2 score. Both study arms were compared to reference ground truth (GT) established by the consensus of two breast subspecialists, ground truthers (GTer), who reviewed the slides digitally. One site recorded the time taken for HER2 scoring of both arms to determine the efficacy of the HER2 AI solution. Results: The initial interobserver agreement among expert GT pathologists for each HER2 score, 0, 1+, 2+, 3+, were 83%, 63.8%, 46%, and 92.6, respectively. At the clinically meaningful cut-offs, the reader pathologists’ performance improved with the AI support. At the 0 vs. 1+/2+/3+ and 0/1+ vs 2+/3+ clinical cutoffs, the readers' accuracy was 86.3% vs 88.6% and 93.7%, 93.8% with and without AI support, respectively. The AI use contributed to approximately 12%-time savings for the readers at the medical center. The AI solution demonstrated high accuracy for HER2 scoring, with 89.2% agreement with GT for 0 vs 1+/2+/3+ scores and 93.1% for 0/1+ vs 2+/3+ cutoff. The results will be updated in the final submission with the additional site data. The reader feedback survey regarding the solution’s usability will also be presented. Conclusions: The pathologists demonstrated improvements in consistency, evidenced by inter-reader agreement and accuracy with the aid of the AI. The AI solution can improve efficiency toward accurate HER2 scoring. [Table: see text]

Supporting customer care and analytics with personal AI assistant: Case study of a food wholesale industry

In this paper, we adopt an exploratory, descriptive single-case study design in a food wholesale industry to illustrate (i) how the personal AI assistant adds value to the sales process and the company, and (ii) how human-like top expert AI suggestions enhance sales people competences. The significance of the study stems from its positioning in the identified research gap by presenting the scalable best product-market fit AI solution for hyper-personalized customer care in the sales process. In addition, the findings of a case study are discussed in comparison with previous related research, providing with technological and organizational AI characteristics systematization.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies. This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels. Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness. This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

MPIGAN: An end-to-end deep based generative framework for high-resolution magnetic particle imaging reconstruction.

Magnetic particle imaging (MPI) is a recently developed, non-invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X-space are two commonly used MPI reconstruction methods, where the former is extremely time-consuming and the latter usually produces blurry images. Currently, we proposed an end-to-end machine learning framework to reconstruct high-resolution MPI images from 1-D voltage signals directly and efficiently. The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291597 pairs of high-resolution 2-D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high-resolution MPI image reconstruction. Experiment results showed that, MPIGAN exhibited remarkable abilities in high-resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X-space in recovering the fine-scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high-quality MPI images. Our study provides a promising AI solution for end-to-end, efficient, and high-resolution magnetic particle imaging reconstruction.

Fake News Detection

Abstract: Fake News is a Global problem and is constantly increasing. There are too many people that goes through a fake news and constantly making a new and fake perspective over a subject. To develop a sustainable AI solution is the market demand for many news websites and social platform. The most the work in this era are developed on Backdated Neural Network Classifiers. To engage with latest Model over a highly rated dataset like ISOT Dataset can be a sustainable solution for Fake News Detection. BERT (Bidirectional Encoder Representations from Transformers) can dynamically calculate every input and output element and their weightage. Combining this technology with the existing ones will help to build a sustainable product with a very novel cause to eradicate and control fake news over internet. It will help to detect, eliminate and try to prevent as much as possible threats causing by Fake News

Optimizing type 2 diabetes management: AI-enhanced time series analysis of continuous glucose monitoring data for personalized dietary intervention.

Despite advanced health facilities in many developed countries, diabetic patients face multifold health challenges. Type 2 diabetes mellitus (T2DM) go along with conspicuous symptoms due to frequent peaks, hypoglycemia <=70 mg/dL (while fasting), or hyperglycemia >=180 mg/dL two hours postprandial, according to the American Diabetes Association (ADA)). The worse effects of Type 2 diabetes mellitus are precisely associated with the poor lifestyle adopted by patients. In particular, a healthy diet and nutritious food are the key to success for such patients. This study was done to help T2DM patients improve their health by developing a favorable lifestyle under an AI-assisted Continuous glucose monitoring (CGM) digital system. This study aims to reduce the blood glucose level fluctuations of such patients by rectifying their daily diet and maintaining their exertion vs. food consumption records. In this study, a well-precise prediction is obtained by training the ML model on a dataset recorded from CGM sensor devices attached to T2DM patients under observation. As the data obtained from the CGM sensor is time series, to predict blood glucose levels, the time series analysis and forecasting are done with XGBoost, SARIMA, and Prophet. The results of different Models are then compared based on performance metrics. This helped in monitoring various trends, specifically irregular patterns of the patient's glucose data, collected by the CGM sensor. Later, keeping track of these trends and seasonality, the diet is adjusted accordingly by adding or removing particular food and keeping track of its nutrients with the intervention of a commercially available all-in-one AI solution for food recognition. This created an interactive assistive system, where the predicted results are compared to food contents to bring the blood glucose levels within the normal range for maintaining a healthy lifestyle and to alert about blood glucose fluctuations before the time that are going to occur sooner. This study will help T2DM patients get in managing diabetes and ultimately bring HbA1c within the normal range (<= 5.7%) for diabetic and pre-diabetic patients, three months after the intervention.

The algorithm journey map: a tangible approach to implementing AI solutions in healthcare

When integrating AI tools in healthcare settings, complex interactions between technologies and primary users are not always fully understood or visible. This deficient and ambiguous understanding hampers attempts by healthcare organizations to adopt AI/ML, and it also creates new challenges for researchers to identify opportunities for simplifying adoption and developing best practices for the use of AI-based solutions. Our study fills this gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System. We conducted 20 interviews with the team of engineers and scientists that led the multi-year effort to build the tool, integrate it into practice, and maintain the solution. This “Algorithm Journey Map” enumerates all social and technical activities throughout the AI solution’s procurement, development, integration, and full lifecycle management. In addition to mapping the “who?” and “what?” of the adoption of the AI tool, we also show several ‘lessons learned’ throughout the algorithm journey maps including modeling assumptions, stakeholder inclusion, and organizational structure. In doing so, we identify generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings. We expect that this effort will further the development of best practices for operationalizing and sustaining ethical principles—in algorithmic systems.

AI-Based Solution for Sustainability Tracing for Companies

Many companies look for novel ways to trace their operational sustainability. The application of AI to analyze and make sense of the big data the company holds represents one promising approach for this aim. The authors study how one can set and design an AI-based solution for improving the sustainability of complex business processes and decision-making in companies of different types. First, they provide a general analysis of current frameworks for measuring adherence to sustainability goals for companies, then they present a conceptual framework and architecture design for an AI-enabled sustainability service for companies. The implications of our research suggest that AI can provide distinct functions: (a) automation: taking big data from different departments and analyzing them with the aim of tracing the sustainability of the company; (b) support: to help decision-making and create relevant insights for stakeholders that are coherent with defined sustainability decision criteria. To the authors' knowledge, no previous research has provided analysis and design of such AI solution for companies.

Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals

The advent of AI has ushered in a new era of patient care, but with it emerges a contentious debate surrounding accountability for algorithmic medical decisions. Within this discourse, a spectrum of views prevails, ranging from placing accountability on AI solution providers to laying it squarely on the shoulders of healthcare professionals. In response to this debate, this study, grounded in the mutualistic partner choice (MPC) model of the evolution of morality, seeks to establish a configurational framework for cultivating felt accountability towards AI among healthcare professionals. This framework underscores two pivotal conditions: AI ethics enactment and trusting belief in AI and considers the influence of organizational complexity in the implementation of this framework. Drawing on Fuzzy-set Qualitative Comparative Analysis (fsQCA) of a sample of 401 healthcare professionals, this study reveals that a) focusing justice and autonomy in AI ethics enactment along with building trusting belief in AI reliability and functionality reinforces healthcare professionals’ sense of felt accountability towards AI, b) fostering felt accountability towards AI necessitates ensuring the establishment of trust in its functionality for high complexity hospitals, and c) prioritizing justice in AI ethics enactment and trust in AI reliability is essential for low complexity hospitals.

Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation

BackgroundChest radiography (CXR) is still of crucial importance in primary diagnostics, but interpretation poses difficulties at times. Research QuestionCan a convolutional neural network (CNN)-based AI system that interprets CXRs add value in an emergency unit (EU) setting? Study Design and MethodsA total of 563 CXRs acquired in the EU of a major university hospital were retrospectively assessed twice by three board-certified radiologists (BCRs), three radiology residents (RRs), and three EU-experienced non-radiology residents (NRRs) employing a two-step reading process: (1) without AI support (woAI), (2) with AI support providing additional images with AI overlays (wAI). Suspicion of four suspected pathologies (pleural effusion, pneumothorax, consolidations suspicious for pneumonia, nodules) was reported on a 5-point confidence scale. BCRs’ confidence scores were converted into four binary reference standards (RFS I-IV) of different sensitivities. RRs' and NRRs' performances woAI/wAI were statistically compared using receiver operating characteristics (ROCs), Youden statistics and operating point metrics derived from fitted ROC curves. ResultsNRRs could significantly improve performance, sensitivity and accuracy wAI in all four pathologies tested. E.g., in the most sensitive RFS IV, NRR consensus improved the AUC (mean, 95%-confidence-interval) in the detection of the time-critical pathology pneumothorax from 0.846 (0.785-0.907) woAI to 0.974 (0.947-1.000) wAI [p<0.001], which represented a gain of 30% in sensitivity and 2% in accuracy (while maintaining an optimized specificity). The most pronounced effect was observed in nodule detection, with NRR wAI improving sensitivity by 53% and accuracy by 7% (AUC woAI: 0.723 [0.661-0.785], wAI: 0.890 [0.848-0.931], p<0.001). The RR consensus wAI showed smaller, mostly non-significant gains in performance, sensitivity and accuracy. InterpretationIn an EU setting without 24/7 radiology coverage, the presented AI solution features an excellent clinical support tool to non-radiologists, similar to a second reader, and allows for a more accurate primary diagnosis and thus earlier therapy initiation.