Application Domains Research Articles

A systematic literature review of recent advances on context-aware recommender systems

AbstractRecommender systems are software mechanisms whose usage is to offer suggestions for different types of entities like products, services, or contacts that could be useful or interesting for a specific user. Other ways have been explored in the field to enhance the power of these systems by integrating the context as an additional attribute. This inclusion tries to extract the user preferences more accurately taking into account multiple components such as temporal, spatial, or social ones. Notwithstanding the magnitude of context-awareness in this area, the research community is in agreement with the lack of framework for context information and how to integrate it into recommender systems. Under this premise, this paper focuses on a comprehensive systematic literature review of the state-of-the-art recommendation techniques and their characteristics to benefit from contextual information. The following survey presents the following contributions as outcomes of our study: (i) determine a framework where multiple aspects are taken into account to have a clear definition of context representation, (ii) the techniques used to incorporate context, and (iii) the evaluation of these methods in terms of reproducibility and effectiveness. Our review also covers some crucial topics about context integration, classification of the contexts, application domains, and evaluation of the used datasets, metrics, and code implementations, where we observed clear shiftings in algorithmic and evaluation trends towards Neural Network approaches and ranking metrics, respectively. Just as importantly, future research opportunities and directions are exposed as final closure, standing out the exploitation of various data sources and the scalability and customization of existing solutions.

Offline supervisory control synthesis: taxonomy and recent developments

AbstractThis paper surveys recent advances in supervisory control theory since its 30th anniversary in 2017. We performed a systematic literature search and selected 272 relevant papers, with a focus on automata-based approaches. These were fitted into coherent narratives, based in part on a taxonomy of supervisor synthesis methods. The obtained papers show the fast and vast progress of the field, where recent research covers a wide range of new synthesis optimization techniques, different aspects such as fault tolerance, security, and timing, and new and diverse application domains. This survey refrains from in-depth descriptions of techniques, instead focusing on high-level contributions of recent works, how they relate to each other, and future directions for research.

Investigation of the Effective Numerical Model for Seismic Response Analysis of Concrete-Faced Rockfill Dam on Deep Overburden

The construction of high rockfill dams on deep overburden in seismically active regions poses significant challenges. Currently, there are no standardized guidelines for defining the computational domain range in seismic analysis, necessitating the establishment of a universally applicable computational domain range that optimizes the balance between computational accuracy and efficiency. This has critical engineering implications for the seismic analysis of rockfill dams on deep overburden. This study employed the seismic wave input method to consider the dynamic interaction between the dam, overburden, and infinite domain. A systematic investigation was conducted on a concrete-faced rockfill dam (CFRD) constructed on deep overburden, considering the influences of overburden thickness, dam height, overburden properties, soil layer configuration, ground motion intensity, and the frequency content of the seismic waves. The acceleration response and seismic deformation of the dam were analyzed. Subsequently, the computational domain range corresponding to various levels of acceptable engineering precision was established. The results indicated that the lateral boundary length should extend a minimum distance equal to the sum of 3 times the overburden depth and 1.2 times the maximum dam height. Additionally, the depth below the overburden–bedrock interface should extend at least 1.2 times the maximum dam height. This study provides a crucial foundation for determining the optimal computational domain range in the seismic analysis of rockfill dams constructed on deep overburden.

Systematic Literature Review of Ecological Discourse Analysis From 2014 to 2023

One of the crucial foci of contemporary linguistics is upon the complex human-nature relationship within the Anthropocene. An essential methodology of eco-linguistics is Ecological Discourse Analysis (EDA), a field which combines linguistics and ecology. The philosophical underpinning of EDA is rooted in an ecological thematic framework, emphasising the significance of biodiversity and sustainability in the natural world. Despite gaining attention over the past decade, EDA still lacks a comprehensive explanation to adequately and accurately explain the fundamental assertion of its philosophical framework: it gives undue prominence to language biodiversity and sustainability. This systematic literature review, conducted using the PRISMA 2020 paradigm, covers studies from 2014 to 2023. It examines 38 works on EDA across several genres. Further clues illustrate the application of EDA to various genres, often informed by theoretical frameworks associated with systemic functional linguistics, cognitive linguistics, and corpus linguistics. EDA research concentrates on ecological discourse and advocating for protection, exposing the lack of nomenclature, analytical framework, application domain, theoretical framework, and objectives. Furthermore, EDA faces challenges in effectively addressing ecological issues within discourse construction because it does not have a sound theoretical paradigm or enough systematicity. Furthermore, the texts analysed using EDA predominantly focus on ecological discourse, with only a few studies incorporating non-ecological literary texts. This underscores the necessity of expanding the scope of ecological linguistic research.

Surface Nanopatterning and Structural Coloration of Liquid Metal Gallium Through Hypergravity Nanoimprinting

AbstractNanoscale structuring of gallium‐based liquid metals has emerged as a promising approach for generating unique properties and functionalities in advanced materials and devices. However, their exceptionally high surface tension presents significant challenges for achieving surface nanostructuring using conventional fabrication techniques. Here, a hypergravity nanoimprinting method is introduced, which harnesses horizontal centrifugation to generate hypergravity fields that drive liquid gallium into nanoscale cavities on an elastic polymer stamp surface at subzero temperatures, where it solidifies and preserves imprinted features down to 100 nm lateral resolution. This surpasses previous limits of gallium patterning, enabling phenomena such as iridescent structural colors with a wide range of hues and high saturation. Numerical simulations reveal the intricate fluid dynamic behaviors and interfacial interactions during the imprinting process, providing valuable insights for process optimization and control. By synergistically combining advanced soft lithography techniques with the reversible solid‐liquid phase transition properties of liquid metals, hypergravity nanoimprinting offers a practical nanofabrication approach, facilitating the development of next‐generation nanoscale gallium devices with finely structured nanofeatures across diverse application domains, such as nanoelectronics and photonic metamaterials.

Tiling Robotics: A New Paradigm of Shape‐Morphing Reconfigurable Robots

The ability of reconfigurable robots to adapt to varying tasks and environments furnishes versatility and efficiency in their operations. In this article, tiling robots are introduced as a novel paradigm of shape‐morphing reconfigurable robots, defining them as polyform‐inspired machines capable of transforming between at least two polymorphic shapes. In the study, the existing and future designs of tiling robots by varying base shapes and polygon selections are explored, identifying a significant gap for further exploration of polyforms in their design. The various reconfiguration‐enabling mechanisms and locomotion mechanisms of tiling robots are comparatively analyzed. Summarized electromechanical developments, along with a proposed generalized kinematic model and control scheme, contribute to a comprehensive understanding of tiling robots. A comparison of tiling robots with other established reconfigurable robots is conducted to position tiling robotics within the broader landscape of reconfigurable robotics. The introduction of a new naming convention addresses the absence of a standardized nomenclature for tiling robots. In this article, highlighting the current focus on area coverage in autonomy algorithms of tiling robots, future developments in diverse application domains like logistics, entertainment, and education are anticipated, emphasizing the adaptability of tiling robots as a critical feature for their proliferation across various domains.

Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance

This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and Phik. This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others.

Leveraging Tropical Algebra to Assess Trustworthy AI

Given the complexity of the application domain, the qualitative and quantifiable nature of the concepts involved, the wide heterogeneity and granularity of trustworthy attributes, and in some cases the non-comparability of the latter, assessing the trustworthiness of AI-based systems is a challenging process. In order to overcome these challenges, the Confiance.ai program proposes an innovative solution based on a Multi-Criteria Decision Aiding (MCDA) methodology. This approach involves several stages: framing trustworthiness as a set of well-defined attributes, exploring attributes to determine related Key Performance Indicators (KPI) or metrics, selecting evaluation protocols, and defining a method to aggregate multiple criteria to estimate an overall assessment of trust. This approach is illustrated by applying the RUM methodology (Robustness, Uncertainty, Monitoring) to ML context, while the focus on aggregation methods are based on Tropical Algebra.

Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).

A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all considered carefully, giving full consideration to the OECD (Organization for Economic Co-operation and Development) principles for QSAR acceptability in regulation during the model construction and assessment process. Nine variables were selected through the forward stepwise regression method and used as inputs to construct both linear and nonlinear models. The obtained models were validated internally and externally. Generally, machine learning-based methods, namely support vector machine (SVM), random forest (RF), and projection pursuit regression (PPR), perform better than the multiple linear regression (MLR) model. The statistical results (R2 = 0.682-0.933, Q2LOO = 0.604-0.659, Q2F1 = 0.740-0.796, CCC = 0.861-0.882) of the developed models show that they are robust, reliable, reproducible, accurate and predictive. Comparatively, the RF model performs best, giving predictive correlation coefficient Q2 of 0.814, root mean squared error (RMSE) of 0.658 and mean absolute error (MAE) of 0.534 for the test set, respectively. The RF model (as well as SVM and PPR models) was visualized and explained by using the SHapley Additive explanation (SHAP) analysis to enhance its transparency and credibility. In addition, the applicability domain (AD) range of the RF model was characterized by the Williams plot and the tree manifold approximation and projection (TMAP) technology was utilized to illustrate similarity and diversity of the entire data space, to assist in the analysis of the outliers. Activity cliff detection was investigated by using Arithmetic Residuals in K-groups Analysis (ARKA) descriptors. It was found that none of the pesticides was identified as an activity cliff in the training set or a potential prediction cliff in the test set. Therefore, the RF model fulfills each OECD principle in regulation for QSAR models. The research in this work will aid in the in silico QSAR prediction of the acute toxicity to Sheepshead minnow (Cyprinodon variegatus) for untested and new toxic pesticides and can also be extended to other studies.

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive data set comprising parameters across multiple species. Building upon this groundwork, we introduced the PKStack ensemble model to predict PK parameters across diverse species. PKStack integrates a variety of base models and includes uncertainty in its predictions. We also manually collected PK data from animals as an external test set. We predicted a total of 45 tasks for nine PK parameters in five species, and in general, the prediction accuracy was better for intravenous injections, including parameters such as human Vd (R2 = 0.72, RMSE = 0.31), human CL (R2 = 0.52, RMSE = 0.32), and others. In addition to predictive accuracy, we also considered the interpretability of the results and the definition of the model's application domain. Based on the findings, our model has great potential for practical applications in drug discovery.

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

Chemical-Specific T Cell Tests Aim to Bridge a Gap in Skin Sensitization Evaluation

T cell activation is the final key event (KE4) in the adverse outcome pathway (AOP) of skin sensitization. However, validated new approach methodologies (NAMs) for evaluating this step are missing. Accordingly, chemicals that activate an unusually high frequency of T cells, as does the most prevalent metal allergen nickel, are not yet identified in a regulatory context. T cell reactivity to chemical sensitizers might be especially relevant in real-life scenarios, where skin injury, co-exposure to irritants in chemical mixtures, or infections may trigger the heterologous innate immune stimulation necessary to induce adaptive T cell responses. Additionally, cross-reactivity, which underlies cross-allergies, can only be assessed by T cell tests. To date, several experimental T cell tests are available that use primary naïve and memory CD4+ and CD8+ T cells from human blood. These include priming and lymphocyte proliferation tests and, most recently, activation-induced marker (AIM) assays. All approaches are challenged by chemical-mediated toxicity, inefficient or unknown generation of T cell epitopes, and a low throughput. Here, we summarize solutions and strategies to confirm in vitro T cell signals. Broader application and standardization are necessary to possibly define chemical applicability domains and to strengthen the role of T cell tests in regulatory risk assessment.

Unveiling the interspecies correlation and sensitivity factor analysis of rat and mouse acute oral toxicity of antimicrobial agents: first QSTR and QTTR Modeling report

Abstract This study aims to identify toxic potential and environmental hazardousness of antimicrobials. In this regard, the available experimental toxicity data with rat and mouse acute oral toxicity have been gathered from ChemID Plus database (n = 202) and subjected to data curation. Upon the data curation 51 and 68 compounds were left for the rat and mouse respectively for the modeling. The quantitative structure toxicity relationship (QSTR) and interspecies correlation analysis by quantitative toxicity-toxicity relationship (QTTR) modeling was approached in this study. The models were developed from 2D descriptors under OECD guidelines by using multiple linear regressions (MLR) with genetic algorithm (GA) for feature selection as a chemometric tool. The developed models were robust (Q2LOO = 0.600–0.679) and predictive enough (Q2Fn = 0.626–0.958, CCCExt = 0.840–0.893). The leverage approach of applicability domain (ad) analysis assures the model’s reliability. The antimicrobials without experimental toxicity values were classified as high, moderate and low toxic based on prediction and ad. The occurrence of the same classification from QSTR and QTTR models revealed the reliability of QTTR models.Finally, the applied “sensitivity factor analysis” typifies the sensitivity of chemicals toward each species. Overall, the first report will be helpful in the toxicity assessment of upcoming antimicrobials in rodents.

Quality of clinical practice guidelines for frozen shoulder: a systematic review.

Perform a systematic critical appraisal of current clinical practice guidelines (CPGs) for frozen shoulder. Systematic review of CPGs (PROSPERO number CRD42022368775). Inclusion criteria- CPGs written in English providing guidance on the evaluation and or treatment for frozen shoulder, traumatic injury and neurologic CPGs were excluded.Relevant studies were assessed for inclusion and selected studies were identified from PubMed, EMBASE, Scopus and CINAHL databases. The search strategy was developed by a biomedical librarian, performed on October 9, 2024. Data were extracted from the selected CPGs and underwent quality assessment using the Appraisal of Guidelines for Research and Evaluation (AGREE) II. The search resulted in 38,428 studies and 2 CPGs were retained for appraisal. The mean overall AGREE II score was 75% (SD = 5.7). Lowest mean scores were found in the applicability (27% SD = 24.0) and editorial independence (48% SD = 14.1) domains. The highest domain scores were found in scope and purpose (92% SD = 7.8) and clarity and presentation (79% SD = 9.9). One CPG was rated as low quality based on a priori criteria and ultimately one higher quality CPG was recommended. Given the advances in research that have developed over the last decade pertaining to the evaluation and treatment of frozen shoulder there is a critical need for an up to date, evidence informed, high quality CPG in order to identify gaps in our knowledge that the global research community should address.

Screening for Depression Using Natural Language Processing: Literature Review.

Depression is a prevalent global mental health disorder with substantial individual and societal impact. Natural language processing (NLP), a branch of artificial intelligence, offers the potential for improving depression screening by extracting meaningful information from textual data, but there are challenges and ethical considerations. This literature review aims to explore existing NLP methods for detecting depression, discuss successes and limitations, address ethical concerns, and highlight potential biases. A literature search was conducted using Semantic Scholar, PubMed, and Google Scholar to identify studies on depression screening using NLP. Keywords included "depression screening," "depression detection," and "natural language processing." Studies were included if they discussed the application of NLP techniques for depression screening or detection. Studies were screened and selected for relevance, with data extracted and synthesized to identify common themes and gaps in the literature. NLP techniques, including sentiment analysis, linguistic markers, and deep learning models, offer practical tools for depression screening. Supervised and unsupervised machine learning models and large language models like transformers have demonstrated high accuracy in a variety of application domains. However, ethical concerns related to privacy, bias, interpretability, and lack of regulations to protect individuals arise. Furthermore, cultural and multilingual perspectives highlight the need for culturally sensitive models. NLP presents opportunities to enhance depression detection, but considerable challenges persist. Ethical concerns must be addressed, governance guidance is needed to mitigate risks, and cross-cultural perspectives must be integrated. Future directions include improving interpretability, personalization, and increased collaboration with domain experts, such as data scientists and machine learning engineers. NLP's potential to enhance mental health care remains promising, depending on overcoming obstacles and continuing innovation.

Bridging IoT Protocols with the Web of Things: A Path to Enhanced Interoperability

Nowadays, the Internet of Things (IoT) is reshaping numerous application domains. Amidst the different communication protocols currently available (MQTT, CoAP, AMQP, DPWS, etc.) and the abundance of management platforms, the IoT domain has stumbled into vertical silos of proprietary systems hindering interoperability. Within this intricate and fragmented ecosystem, the need for an intelligent and scalable architecture promoting interoperability becomes imperative to maximize the potential of IoT. This paper introduces a system designed to enhance the convergence of IoT protocols (e.g., MQTT, CoAP, AMQP, etc.) with the Web of Things (WoT) paradigm. Our proposed gateway-based solution integrates two systems: Stack4Things (S4T) and Data eXchange Mediator Synthesizer (DeXMS). The role of the latter is to adapt, at the gateway level, the IoT protocols and expose the resources/functionalities of the IoT devices as RESTful APIs using HTTP. Meanwhile, the former (i.e., S4T), leveraging its Dynamic DNS system, ensures that these RESTful resources (exposed at the gateway) are accessible over the Web using publicly routable Uniform Resource Locators (URLs) even when the gateway is deployed behind networking middle boxes (e.g., NATs and firewalls).

Predictive Analytics Technique based on hybrid Sampling to manage Unbalanced Data in Smart Cities

A smart city is deemed smart enough because it has the capability to make decisions on its own. Artificial intelligence needs a lot of data from the physical world to make correct decisions. IoT sensor devices collect data from the surroundings, which is further used for predictive analytics. Collected data may be balanced or imbalanced. Unbalanced data used for decision-making without any pre-processing may lead to ravaging results. This paper proposes a novel predictive analytical technique to manage unbalanced data. A pipeline is designed using Principal Component Analysis (PCA), a hybrid sampling method, and a Machine Learning (ML) prediction method. SMOTE + ENN, a hybrid data balancing method, is used to specify imbalanced data to a balanced state. ML method is applied to form clusters and make predictions over the dataset. A large Smart City IoT dataset having 4,05,184 records has been used in this study. The proposed technique is used to predict the presence of a person in the vicinity of IoT devices. Evaluation parameters such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC)/Receiver Operating Characteristic (ROC) curve are used to evaluate the proposed approach. Accuracy, Precision, Recall, F1-score, and AUC obtained using the proposed technique for cluster 0 are 0.79, 1.0, 0.79, 0.87, and 0.88 and for cluster 1 are 0.86 0.99, 0.86, 0.92, and 0.92, respectively. In view of the encouraging results, the proposed technique may prove to be a good choice to help in decision-making in different application domains in real life.

Quality appraisal of clinical guidelines for peripherally inserted central catheter-related thrombosis prophylaxis in patients: a systematic review.

To evaluate and analyse the quality of clinical practice guidelines for Peripherally Inserted Central Catheter-related thrombosis (PICC-related thrombosis) to identify the most current and effective prophylactic measures recommended in the guidelines. Scoring and analysis of the guidelines using the Appraisal of Guidelines for Research and Evaluation II (AGREE II). Cochrane Library, PubMed, EMBASE, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Chinese databases (China National Knowledge Infrastructure and Wan Fang) and the relevant websites of the guideline were searched through 8 August 2024. Studies that primarily clinical practice guidelines on the prevention of PICC-related thrombosis were included. Two reviewers independently screened the searched items and extracted data and scored documents using AGREE II. Findings were summarised in Grading of Recommendation, Assessment, Development and Evaluation (GRADE) evidence profiles and synthesised qualitatively. The analysis incorporated a total of nine guidelines, all rated as 'recommended' or 'recommended with modifications'. Standardised scores revealed elevated performance in the domains of Scope and Purpose, Clarity of Presentation and Editorial Independence. Conversely, the Stakeholder Involvement and Applicability domains yielded the lowest average standardised scores. Disparities in standardised scores across guidelines were particularly evident in the domains of Rigour of Development, Stakeholder Involvement and Applicability. The agreement between the two appraisers was almost perfect (intraclass correlation coefficients higher than 0.80). A considerable proportion of recommendations relied on evidence of low-quality, in certain instances, were derived from expert opinions within working groups. The review reveals that a significant portion of recommendations relies on low-quality evidence. Guideline developers are urged to prioritise methodological quality, with a specific focus on refining Stakeholder Involvement and Applicability domains. Addressing these aspects will enhance the overall quality and reliability of PICC-related thrombosis prevention guidelines. One potential way to mitigate these challenges is to endorse a standardised approach to guideline development and to synthesise reliable clinical evidence to reduce variation in recommendations. CRD42023495519.

Digital transformation in public-private collaborations: The success of humanitarian supply chain operations

Recent years have seen the extensive use of big data analytics, related technological infrastructure, and machine learning applications for digital transformation. The resource dependency related to data-driven applications elicits public-private collaborations (PPCs) between governments and private or non-government organizations (NGOs) for value creation. Such collaborations are effective for the success of humanitarian supply chain operations (HSCOs), particularly in the event of large-scale disasters. By building on resource dependence theory (RDT), our study explores the links between digital transformation, PPCs, and HSCO success. Using structural equation modeling on data collected from 224 key decision-makers and experts, we found that digital transformation mediates the relationship between private-NGO collaborations and HSCO success while host government support moderates it. Our study thus makes an original contribution to RDT and the emerging domains of contemporary digital and data-driven applications in HSCO. The implications and future research directions arising from this study are also discussed in this research paper.

ChatCAD+: Toward a Universal and Reliable Interactive CAD Using LLMs.

The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face at least two limitations: (1) From the perspective of a radiologist, existing studies typically have a restricted scope of applicable imaging domains, failing to meet the diagnostic needs of different patients. Also, the insufficient diagnostic capability of LLMs further undermine the quality and reliability of the generated medical reports. (2) Current LLMs lack the requisite depth in medical expertise, rendering them less effective as virtual family doctors due to the potential unreliability of the advice provided during patient consultations. To address these limitations, we introduce ChatCAD+, to be universal and reliable. Specifically, it is featured by two main modules: (1) Reliable Report Generation and (2) Reliable Interaction. The Reliable Report Generation module is capable of interpreting medical images from diverse domains and generate high-quality medical reports via our proposed hierarchical in-context learning. Concurrently, the interaction module leverages up-to-date information from reputable medical websites to provide reliable medical advice. Together, these designed modules synergize to closely align with the expertise of human medical professionals, offering enhanced consistency and reliability for interpretation and advice. The source code is available at GitHub.