Improving machine-learning development in allergology: bridging the gap between open-access and cohort-based databases.

Technological developments present a variety of forms of interactive media in physics learning. Comics and artificial intelligence (AI) are two innovative media genres that are popularly used to enhance learning. However, a general review of the learning effects of various previous researchers on the effects of comics and AI on physics learning is still limited. This literature review aims to identify the impacts and challenges of implementing comics and AI in physics learning. This literature review goes through five stages, including determining the research question, literature search, article selection, outlining the information from the articles, and literature writing. Articles are sourced from open-access databases, Scorpus, ProQuest, ERIC and Google Scholar. Articles published between 2020 and 2024 are found through searches using keywords in the abstract and title. The search results in as many as 23 articles worth reviewing. Based on the review, comics and AI have proven effectiveness in enhancing physics learning by increasing interest, motivation, and student involvement in learning and understanding concepts in physics. These media have great potential to support interactive and communicative learning. Both media have impacts, advantages, weaknesses, and challenges in their application. AI can give lead to plagiarism in learning, and uncontrolled comics affect students' focus on learning. The implementation of these two learning media has a positive effect on improving learning outcomes. This medium can teach complex subjects in an accessible and fun way. Comics provide interactive and communicative learning that supports independent learning for students. In conclusion, comics and AI positively impact students' physics learning. This literature review provides information about research objects, issues, and conclusions for other researchers regarding the development of comics and AI in physics learning.

BackgroundAn increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers.MethodsWe retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building.ResultsAmong LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99).ConclusionsThese results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

BackgroundDifferences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance.MethodsThe decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast.ResultsThe algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners.ConclusionsThe results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development – Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

Artificial intelligence (AI) has created tremendous opportunities to improve thyroid cancer care. We used the "artificial intelligence thyroid cancer" query to search the PubMed database until May 31, 2025. We highlight a set of high-impact publications selected based on technical innovation, large generalizable training datasets, and independent and/or prospective validation of AI. We review the key applications of AI for diagnosing and managing thyroid cancer. Our primary focus is on using computer vision to evaluate thyroid nodules on thyroid ultrasound, an area of thyroid AI that has gained the most attention from researchers and will likely have a significant clinical impact. We also highlight AI for detecting and predicting thyroid cancer neck lymph node metastases, digital cyto- and histopathology, large language models for unstructured data analysis, patient education, and other clinical applications. We discuss how thyroid AI technology has evolved and cite the most impactful research studies. Finally, we balance our excitement about the potential of AI to improve clinical care for thyroid cancer with current limitations, such as the lack of high-quality, independent prospective validation of AI in clinical trials, the uncertain added value of AI software, unknown performance on non-papillary thyroid cancer types, and the complexity of clinical implementation. AI promises to improve thyroid cancer diagnosis, reduce health care costs and enable personalized management. High-quality, independent prospective validation of AI in clinical trials is lacking and is necessary for the clinical community's broad adoption of this technology.

The materials project – Database and toolbox for materials science So far, numerous databases have been built around the world that summarize a large number of physical and chemical properties, thermodynamical diagrams, crystal structures, and some other useful experimental and calculated data. It is extremely useful for checking and confirming various properties of known materials and, conversely, for discovering promising candidates for functions and properties of interest. Furthermore, one of the remarkable recent trends is employment of deep learning, which is known as the 3rd generation of artificial intelligence, to explore some knowledge and models using already stored big data and some optimized algorithms. The system also includes a platform for quantum mechanical calculation. The Materials Project, established in 2011 by Professor Kristin Persson of Lawrence Berkeley National Laboratory, is an open-access database. Anyone can register as a user and start using it for free of charge. More than 120,000 users have already registered. From the perspective of X-ray experts, the system should be very interesting, because it includes many experimentally collected X-ray absorption spectra, the crystal structure data, and the related quantum mechanical calculation data. The total number of the registered data exceeds 500,000; X-ray spectra and crystal structure data are nearly 20% and 35%, respectively, and others are more theoretical data, such as the density of states, the bond structures and so forth. The Materials Project organizes seminars and annual workshop as well. All of the archives are available on its Youtube site (https://www.youtube.com/c/MaterialsProject). For further information, visit the following page, https://materialsproject.org/

The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.

Military and peace-support operations increasingly depend on medical capability delivered under difficult circumstances: distance, limited number of specialists, disrupted connectivity, and mass-casualty risk. Consequently, military health care systems operate during missions in uniquely constrained and high-risk environments, ranging from remote bases and naval platforms to active combat zones and humanitarian missions. These mission contexts demand rapid, reliable, and scalable medical solutions that can function despite limited personnel, infrastructure, and connectivity. By reviewing existing literature and considering several different programs for military health care using telehealth and artificial intelligence (AI), the key performance indicators are explored to evaluate the synergy of telehealth and AI, following its implementation. Telehealth has become an increasingly important component of modern health care and holds the promise of increasing access to (special) health care without moving the patient but the information. Telehealth also enables continuity of care on missions and has already demonstrated its value in extending medical expertise across distance, while AI is evolving rapidly as a powerful enabler of decision support, automation, and predictive analytics in the medical field. As AI capabilities mature, several medical specialties may benefit, particularly in medical imaging, through faster triage, more consistent interpretation, and better prioritization of scarce specialist time. It also contributes to "decision support "while making proposals based on big data, machine learning and AI (e.g., in a Trauma registry). Telehealth provides the communication backbone that preserves these interactions and enables safe use of AI-enabled support in dispersed operations. The convergence of telehealth and AI in military health care will lead to a strategic capability with increased efficiency and enhance readiness, resilience, access to medical care, and quality of care for military personnel, including faster and more accurate diagnoses and better patient monitoring. Both technologies (AI and telehealth) are expected to continue to advance and play an even larger role in optimizing health care in the military.

BackgroundMedical imaging remains at the forefront of advancements in adopting digital health technologies in clinical practice. Regulator-approved artificial intelligence (AI) clinical decision support systems are commercially available and being embedded into routine practices for radiologists internationally. These decision support solutions show promising clinical validity compared to standard practice conditions; however, their implementation over time and implications on radiologists’ practice are poorly understood.ObjectiveThis paper aims to examine the real-world implementation of an AI clinical decision support tool in radiology through a qualitative evaluation across pre-, peri-, and postimplementation phases. Specifically, it seeks to identify the key contextual, organizational, and human factors shaping adoption and sustainability, to map these influences using the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, and to generate insights that inform evidence-based strategies and policy for integrating AI safely and effectively into public hospital imaging services.MethodsThis prospective study was conducted in a large public tertiary referral hospital in Brisbane, Queensland, Australia. One-to-one participant interviews were undertaken across the 3 implementation phases. Participants comprised radiology consultants, registrars, and radiographers involved in chest computed tomography studies during the study period. Interviews were guided by the NASSS framework to identify contextual factors influencing implementation.ResultsA total of 43 semistructured interviews were conducted across baseline (n=16), peri-implementation (n=9), and postimplementation (n=18) phases, comprising 7 (16%) radiographers, 20 (47%) registrar radiologists, and 16 (37%) consultant radiologists. Across NASSS domains, 56 barriers and 18 enablers were identified at baseline, 55 and 14 during peri-implementation, and 82 and 33 postimplementation. Organizational barriers dominated early phases, while technological issues such as system accuracy, interoperability, and information overload became most prominent during and after rollout. Enablers increased over time, particularly within the technology and value proposition domains, as some clinicians adapted the AI as a secondary safety check. Trust and adoption remained constrained by performance inconsistency, weak communication, and medicolegal uncertainty.ConclusionsThe implementation of AI decision support in radiology is as much an organizational and cultural process as a technological one. Clinicians remain willing to engage, but sustainable adoption depends on consolidating early positive experiences and addressing negative ones, embedding communication and training, and maintaining iterative feedback between users, vendors, and system leaders. Applying the NASSS framework revealed how domains interact dynamically across time, offering both theoretical insight into sociotechnical complexity and practical guidance for hospitals seeking to move from pilot to routine, trustworthy AI integration.

Mechanical ventilation is a life-saving intervention, which, despite being routinely used in ICUs, poses the risk of causing further damage to the lung tissue if the ventilator is set inappropriately. Medical decision support systems may help in optimizing ventilator settings according to therapy goals given by the clinician. Before using the decision support algorithms in commercially available systems, extensive tests are necessary to ensure patient safety and correct decision making. Model-based patient simulators can assist in evaluating such decision support systems by creating different clinical scenarios. We propose a new Java based patient simulator that implements various models of respiratory mechanics, gas exchange and cardiovascular dynamics to form a complex patient model. The implemented models interact with one another to allow simulation of the ventilators influence on various physiological processes. Model simulations are running in real-time and simulation results can be extracted via multiple interfaces. Each of the implemented models has been validated to exhibit physiologically correct behavior. Results of the combined model system also showed to be physiologically plausible.

Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.

<title>Abstract</title> Artificial intelligence (AI) is increasingly acknowledged as a transformational influence in healthcare, including early diagnostic responses, decision support, and therapeutic enhancement. Successful implementation of AI technologies is dependent on the attitudes of healthcare decision-makers (HDMs) who have a central role in influencing institutional and national adoption plans. This research aimed to explore the impact of technology acceptance, AI anxiety, and demographic factors on Jordanian HDMs' attitudes towards AI in healthcare institutions A cross-sectional approach was used, focusing on 152 healthcare decision-makers from governmental, NGO, and academic institutions in Jordan. Data was collected using a structured online questionnaire utilizing three validated instruments: the General AI Attitudes Scale, the Artificial Intelligence Anxiety Scale, and a modified Technology Acceptance Model. Hierarchical multiple linear regression was used to identify determinants of favorable and negative views about AI. The results indicated the most positive feelings toward AI among participants, shown by a mean positive attitude score of 3.94 (SD = 0.59) and a somewhat lower mean negative attitude score of 2.60 (SD = 0.71). Junior HDMs and those employed in academic institutions, NGOs, and governmental organizations had greater positive views compared to their private sector counterparts. Heightened anxiety over AI, especially linked to its learning capacities and sociotechnical ramifications, was strongly associated with negative perceptions. Furthermore, perceived value and ease of use were identified as significant factors influencing positive attitudes towards AI. These results emphasize the need for specialized training and intuitive AI solutions to enhance acceptance and enable the efficient use of AI in healthcare systems.

This study explores the perspectives of Indian library professionals on the use of artificial intelligence (AI) in libraries. It aims to understand their knowledge, awareness, and views on AI and its challenges and opportunities. The research adopts a quantitative approach, using a closed-ended survey to collect data from 386 library professionals in academic institutions across India. The survey covers various aspects, including socio-demographic information, AI knowledge, perspectives on AI in libraries, ethical considerations, and adopting AI tools and services. The findings indicate that Indian library professionals are generally aware of AI and its potential benefits in libraries. They believe that AI can enhance library activities, improve accessibility, and support decision-making. However, there are concerns about AI replacing human intelligence within libraries. Factors such as user privacy, funding, staff expertise, and alignment with institutional goals are essential when implementing AI applications. Ethical considerations, including bias and discrimination, intellectual freedom, and transparency, are also significant concerns. AI tools like smart shelving and optical character recognition are among the technologies already adopted in Indian libraries.

В данной статье представлена модель поддержки принятия решений для управления маркетингом как эффективный инструмент привлечения потребителей. В работе проведена разработка ряда классификаций, которые предназначены для использования при вы-боре входных данных, на основе которых подбирается ряд альтернативных механик: классификационное де-рево целей маркетинговых мероприятий; классификация потребителей по интересам и классификация по-требителей по стадии взаимоотношений с торговым предприятием.Эти классификации положены в основу комплексного алгоритма поддержки принятия решения в выборе механики привлечение потребителей. Визуальный алгоритм является материалом для создания программного продукта в среде, удобной для конкретного предприятия, в данной работе выбраны инструменты офисного пакета Google Docs, веб-ориентированное программное обеспечение. Google-формы имеют удобный интер-фейс, возможность стилизации опроса, наглядную статистику ответов и преобразование их в Google-таблицы.Данный инструмент позволяет учесть основные факторы, влияющие на выбор механики привлечения по-требителей, который позволит повысить эффективность рекламно-акционных мероприятий, снизит веро-ятность ошибки и сократит время самого процесса формирования механики привлечения потребителей, что позволит компании быстро реагировать на внешние факторы.

The article explores the transformative impact of Artificial Intelligence (AI) on&#x0D; engineering, focusing on the evolution over the last decade. AI has become a cornerstone in&#x0D; reliable engineering, influencing various aspects such as predictive maintenance, fault&#x0D; detection, optimization, automation, and decision support. Predictive maintenance, enabled by&#x0D; AI algorithms, revolutionizes traditional approaches by analysing extensive datasets to predict&#x0D; equipment failures, allowing proactive interventions and minimizing downtime. Fault detection&#x0D; and diagnostics benefit from AI's real-time monitoring and early anomaly identification,&#x0D; reducing the risk of catastrophic failures and enhancing overall system reliability. Optimization&#x0D; of complex systems is facilitated by AI's capacity to process vast amounts of data, leading to&#x0D; improved performance and minimized resource consumption. The integration of AI in&#x0D; automation and robotics reshapes manufacturing processes, emphasizing precision and&#x0D; reliability. Simulation and modelling, data analysis, and supply chain optimization are also&#x0D; discussed as vital areas where AI contributes to enhanced reliability. The article highlights the&#x0D; importance of ethical considerations and human oversight in deploying AI responsibly,&#x0D; emphasizing a collaborative synergy between AI and human expertise for continued&#x0D; advancements in engineering solutions.

There has been a steep rise in the use of decision aids enabled by artificial intelligence (AI) for facial identity verification. The use of such systems and the impact of their implementation on human decision-making is still not well understood. The current study aimed to explore factors associated with the design of the paradigm and of the presentation of AI-enabled predictions. Across three pre-registered experiments, we examined the impact of (a) stated AI accuracy, (b) mismatch frequency (i.e. proportion of match and mismatch pairs), and (c) advice type (binary only vs. binary + similarity rating) on performance in a one-to-one face matching task. Participants’ performance generally improved when aided by AI compared to a baseline without decision support. The largest improvement was observed when no information on the AI’s overall accuracy was provided. Further, the frequency of mismatches did not influence performance, but biased responses. Finally, similarity ratings marginally improved overall performance and increased users’ certainty in their decisions, but did not help to dismiss inaccurate predictions. Additionally, two findings were consistent across all experiments. First, participants often failed to dismiss inaccurate AI predictions, resulting in significantly lower performance compared to accurate predictions. Second, on a group level, the human–AI team did not outperform the AI alone, though examination of individual performance showed that some participants were able to exceed the AI’s accuracy. These findings contribute towards determining appropriate design formats for AI prediction in a human-in-the-loop system, so that the performance of the human–AI team can be maximised.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41235-025-00701-x.