La integración de la inteligencia artificial en la educación médica y su impacto en la práctica clínica / The integration of artificial intelligence in medical education and its impact on clinical practice

Introducción: La integración de la inteligencia artificial (IA) en la educación médica redefine paradigmas, optimiza méto-dos y forja una simbiosis tecnológica. Desarrollo: La IA potencia simulaciones clínicas, mejora evaluaciones y desarrolla habilidades blandas, redefiniendo lainteracción médico-paciente. Conclusiones: Aunque persisten desafíos éticos, la colaboración interdisciplinaria y la adaptabilidad son cruciales. La IA marca un hito en la evolución médica al elevar la calidad asistencial y establecer estándares para una colaboración armoniosa entre tecnología y compasión.(AU)

Introduction: The incorporation of artificial intelligence (AI) into medical education redefines paradigms, optimisesmethods and forges a technological symbiosis. Development: AI enhances clinical simulations, improves assessments and develops soft skills, thereby redefining doctor-patient interaction. Conclusions: Although ethical challenges remain, interdisciplinary collaboration and adaptability are crucial. AI marks a milestone in the evolution of medicine by raising the quality of care and setting standards for harmonious collaboration between technology and compassion.(AU)

Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review.

Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.

The Potential for Artificial Intelligence Tools in Residency Recruitment.

The information considered important for the holistic review of residency applications has expanded beyond numerical and discrete data such as grades, test scores, publications, and awards. To conduct such a thorough review requires time and the processing of large amounts of information, which invites the development of new tools to streamline application review. Artificial intelligence (AI) solutions may increase the efficiency of the review process as well as enhance the opportunity to find applicants who may have been overlooked by a traditional review process. These tools also may help applicants find programs that fit their career aspirations, practice interview techniques, and refine their written applications. With the introduction of new technology comes the need to also monitor for potential pitfalls, which will become more critical when adoption begins to accelerate, highlighting the need to both embrace and consistently reassess the use of these innovations in the residency recruitment process.

Performance of ChatGPT on Chinese Master's Degree Entrance Examination in Clinical Medicine.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test's accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. CONCLUSION: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

Verified localization and pharmacognosy of herbal medicinal plants in a combined network framework.

Pharmacognosy from medicinal plants involves the scientific domain of medicinal compounding based on their medicinal properties. Accurate identification of medicinal plants is crucial, especially by examining their leaves. Choosing the wrong plant species for medicinal preparations can have adverse side effects. This study presents a Human-Centered Artificial Intelligence approach for medicinal plant identification, combining a YOLOv7-based Leaf Localizer with a leaf Class Verifier based on DenseNet through a Confidence Score Analyser algorithm. The Confidence Score Analyser ensures reliability by evaluating predicted categories against predefined thresholds, and the ensemble technique through majority voting enhances robustness. An average performance gain of 25.66% sensitivity is observed when comparing the YOLO object detection model with 77.45% precision to the YOLO integrated with the class verifier model with 97.33% precision. Consistent sensitivities are achieved through the ensemble technique, showcasing robustness across diverse scenarios. The final step incorporates automated textual and audio pharmacognosy information about the identified medicinal plant properties and their utility. Real-time applicability as a smart phone application makes this approach invaluable for medicinal plant collectors and experts.

Application of artificial intelligence to eyewitness identification.

Artificial intelligence is already all around us, and its usage will only increase. Knowing its capabilities is critical. A facial recognition system (FRS) is a tool for law enforcement during suspect searches and when presenting photos to eyewitnesses for identification. However, there are no comparisons between eyewitness and FRS accuracy using video, so it is unknown whether FRS face matches are more accurate than eyewitness memory when identifying a perpetrator. Ours is the first application of artificial intelligence to an eyewitness experience, using a comparative psychology approach. As a first step to test system accuracy relative to eyewitness accuracy, participants and an open-source FRS (FaceNet) attempted perpetrator identification/match from lineup photos (target-present, target-absent) after exposure to real crime videos with varied clarity and perpetrator race. FRS used video probe images of each perpetrator to achieve similarity ratings for each corresponding lineup member. Using receiver operating characteristic analysis to measure discriminability, FRS performance was superior to eyewitness performance, regardless of video clarity or perpetrator race. Video clarity impacted participant performance, with the unclear videos yielding lower performance than the clear videos. Using confidence-accuracy characteristic analysis to measure reliability (i.e., the likelihood the identified suspect is the actual perpetrator), when the FRS identified faces with the highest similarity values, they were accurate. The results suggest FaceNet, or similarly performing systems, may supplement eyewitness memory for suspect searches and subsequent lineup construction and knowing the system's strengths and weaknesses is critical.

[Reflections on good manufacturing practice and quality control system of personalized traditional Chinese medicine preparations].

Personalized traditional Chinese medicine(TCM) preparations have entered a stage of rapid development. The key to the healthy development of this industry is to establish a sound manufacturing standard and quality control system. This paper analyzed the characteristics of personalized TCM preparations and drew reference from the quality management standards in the production of commissioned decoctions and oral pastes, on the basis of which the production quality management scheme and cautions for the safe production of personalized TCM preparations was put forward with consideration to various problems that may exist and occur in the production of such preparations. It provided references for formulating the production standards and quality management system of personalized TCM preparations. The production standards and quality control system should develop with the times. In the future, modern technologies such as big data and artificial intelligence should be employed to achieve the automated and intelligent production and establish a sound quality traceability system, online control strategy, and safety management mode of personalized TCM preparations, which will ensure the healthy development of this industry under requirement of good manufacturing practice(GMP).

Using generative AI to investigate medical imagery models and datasets.

BACKGROUND: AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts. METHODS: In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries). FINDINGS: To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities-retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown). INTERPRETATION: Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes. FUNDING: Google.

Exploring the integration of artificial intelligence in the sports tourism industry

Artificial intelligence (AI) has advanced from a theoretical concept to a practical application thanks to the quick development of computer science and information technology. AI, a fundamental component of contemporary civilization, has a growing impact on all facets of daily life, including sports training. Artificial intelligence (AI) can be viewed as a supporting technology that specifically supports athletes' physical education training through methods like data analysis and simulation of training scenarios. Even though research on AI is still in its early stages, it is important to investigate how it may be used in sports training becausethis cutting-edge technology could in some ways make it easier for individuals to train physically. This study begins by reviewing the prior work on AI applications.In, this study investigates three specific situations of AI application in sports training and describes the key concepts based on the core idea and related research findings of AI. This study focuses on the close connection between artificial intelligence (AI) and physical education instruction and emphasises the benefits of AI, such as its use, ease, and innovation. This study creates the appropriate information data interface mode based on the integration of the sports tourist sector and the culture industry. (AU)

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

BACKGROUND: The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC. METHODS: LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed. RESULTS: Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059). CONCLUSION: This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.

An AI healthcare ecosystem framework for Covid-19 detection and forecasting using CronaSona.

The primary purpose of this paper is to establish a healthcare ecosystem framework for COVID-19, CronaSona. Unlike some studies that focus solely on detection or forecasting, CronaSona aims to provide a holistic solution, for managing data and/or knowledge, incorporating detection, forecasting, expert advice, treatment recommendations, real-time tracking, and finally visualizing results. The innovation lies in creating a comprehensive healthcare ecosystem framework and an application that not only aids in COVID-19 diagnosis but also addresses broader health challenges. The main objective is to introduce a novel framework designed to simplify the development and construction of applications by standardizing essential components required for applications focused on addressing diseases. CronaSona includes two parts, which are stakeholders and shared components, and four subsystems: (1) the management information subsystem, (2) the expert subsystem, (3) the COVID-19 detection and forecasting subsystem, and (4) the mobile tracker subsystem. In the proposed framework, a CronaSona app. was built to try to put the virus under control. It is a reactive mobile application for all users, especially COVID-19 patients and doctors. It aims to provide a reliable diagnostic tool for COVID-19 using deep learning techniques, accelerating diagnosis and referral processes, and focuses on forecasting the transmission of COVID-19. It also includes a mobile tracker subsystem for monitoring potential carriers and minimizing the virus spread. It was built to compete with other applications and to help people face the COVID-19 virus. Upon receiving the proposed framework, an application was developed to validate and test the framework's functionalities. The main aim of the developed application, CronaSona app., is to develop and test a reliable diagnostic tool using deep learning techniques to avoid increasing the spread of the disease as much as possible and to accelerate the diagnosis and referral of patients by detecting COVID-19 features from their chest X-ray images. By using CronaSona, human health is saved and stress is reduced by knowing everything about the virus. It performs with the highest accuracy, F1-score, and precision, with consecutive values of 97%, 97.6%, and 96.6%.

Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.

INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this information impacts procedural efficiency is unknown. We performed a retrospective, case-control study to evaluate the hypothesis that AI ECG mapping may reduce time to ablation, procedural duration, and fluoroscopy. MATERIALS AND METHODS: Cases in which system output was used were retrospectively enrolled according to IRB-approved protocols at each site. Matched control cases were enrolled in reverse chronological order beginning on the last day for which the technology was unavailable. Controls were matched based upon physician, institution, arrhythmia, and a predetermined complexity rating. Procedural metrics, fluoroscopy data, and clinical outcomes were assessed from time-stamped medical records. RESULTS: The study group consisted of 28 patients (age 65 ± 11 years, 46% female, left atrial dimension 4.1 ± 0.9 cm, LVEF 50 ± 18%) and was similar to 28 controls. The most common arrhythmia types were atrial fibrillation (n = 10), premature ventricular complexes (n = 8), and ventricular tachycardia (n = 6). Use of the system was associated with a 19.0% reduction in time to ablation (133 ± 48 vs. 165 ± 49 min, p = 0.02), a 22.6% reduction in procedure duration (233 ± 51 vs. 301 ± 83 min, p < 0.001), and a 43.7% reduction in fluoroscopy (18.7 ± 13.3 vs. 33.2 ± 18.0 min, p < 0.001) versus controls. At 6 months follow-up, arrhythmia-free survival was 73.5% in the study group and 63.3% in the control group (p = 0.56). CONCLUSION: Use of forward-solution AI ECG mapping is associated with reductions in time to first ablation, procedure duration, and fluoroscopy without an adverse impact on procedure outcomes or complications.

Accuracy of Information given by ChatGPT for Patients with Inflammatory Bowel Disease in Relation to ECCO Guidelines.

BACKGROUND: As acceptance of artificial intelligence [AI] platforms increases, more patients will consider these tools as sources of information. The ChatGPT architecture utilizes a neural network to process natural language, thus generating responses based on the context of input text. The accuracy and completeness of ChatGPT3.5 in the context of inflammatory bowel disease [IBD] remains unclear. METHODS: In this prospective study, 38 questions worded by IBD patients were inputted into ChatGPT3.5. The following topics were covered: [1] Crohn's disease [CD], ulcerative colitis [UC], and malignancy; [2] maternal medicine; [3] infection and vaccination; and [4] complementary medicine. Responses given by ChatGPT were assessed for accuracy [1-completely incorrect to 5-completely correct] and completeness [3-point Likert scale; range 1-incomplete to 3-complete] by 14 expert gastroenterologists, in comparison with relevant ECCO guidelines. RESULTS: In terms of accuracy, most replies [84.2%] had a median score of ≥4 (interquartile range [IQR]: 2) and a mean score of 3.87 [SD: ±0.6]. For completeness, 34.2% of the replies had a median score of 3 and 55.3% had a median score of between 2 and <3. Overall, the mean rating was 2.24 [SD: ±0.4, median: 2, IQR: 1]. Though groups 3 and 4 had a higher mean for both accuracy and completeness, there was no significant scoring variation between the four question groups [Kruskal-Wallis test p > 0.05]. However, statistical analysis for the different individual questions revealed a significant difference for both accuracy [p < 0.001] and completeness [p < 0.001]. The questions which rated the highest for both accuracy and completeness were related to smoking, while the lowest rating was related to screening for malignancy and vaccinations especially in the context of immunosuppression and family planning. CONCLUSION: This is the first study to demonstrate the capability of an AI-based system to provide accurate and comprehensive answers to real-world patient queries in IBD. AI systems may serve as a useful adjunct for patients, in addition to standard of care in clinics and validated patient information resources. However, responses in specialist areas may deviate from evidence-based guidance and the replies need to give more firm advice.

Traumatic Brain Injury Recovery with Photobiomodulation: Cellular Mechanisms, Clinical Evidence, and Future Potential.

Traumatic Brain Injury (TBI) remains a significant global health challenge, lacking effective pharmacological treatments. This shortcoming is attributed to TBI's heterogeneous and complex pathophysiology, which includes axonal damage, mitochondrial dysfunction, oxidative stress, and persistent neuroinflammation. The objective of this study is to analyze transcranial photobiomodulation (PBM), which employs specific red to near-infrared light wavelengths to modulate brain functions, as a promising therapy to address TBI's complex pathophysiology in a single intervention. This study reviews the feasibility of this therapy, firstly by synthesizing PBM's cellular mechanisms with each identified TBI's pathophysiological aspect. The outcomes in human clinical studies are then reviewed. The findings support PBM's potential for treating TBI, notwithstanding variations in parameters such as wavelength, power density, dose, light source positioning, and pulse frequencies. Emerging data indicate that each of these parameters plays a role in the outcomes. Additionally, new research into PBM's effects on the electrical properties and polymerization dynamics of neuronal microstructures, like microtubules and tubulins, provides insights for future parameter optimization. In summary, transcranial PBM represents a multifaceted therapeutic intervention for TBI with vast potential which may be fulfilled by optimizing the parameters. Future research should investigate optimizing these parameters, which is possible by incorporating artificial intelligence.

ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review.

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians' ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively.

The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis.

Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships in the application of AI to medical diagnostics is essential to further promote its use in clinical practice. In this study, we conducted a bibliometric analysis to explore the evolution of task-specific to general-purpose AI for medical diagnostics. We used the Web of Science database to search for relevant articles published between 2010 and 2023, and applied VOSviewer, the R package Bibliometrix, and CiteSpace to analyze collaborative networks and keywords. Our analysis revealed that the field of AI in medical diagnostics has experienced rapid growth in recent years, with a focus on tasks such as image analysis, disease prediction, and decision support. Collaborative networks were observed among researchers and institutions, indicating a trend of global cooperation in this field. Additionally, we identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power. Challenges to progress in the field include model explainability, robustness, and equality, which will require multi-stakeholder, interdisciplinary collaboration to tackle. Our study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics. With the continuous improvement of AI technology and the accumulation of medical data, we believe that AI will play a greater role in medical diagnostics in the future.

Contribution of Sub-Saharan African medicinal plants to cancer research: Scientific basis 2013-2023.

Cancer incidence and mortality rates are increasing worldwide. Cancer treatment remains a real challenge for African countries, especially in sub-Saharan Africa where funding and resources are very limited. High costs, side effects and drug resistance associated with cancer treatment have encouraged scientists to invest in research into new herbal cancer drugs. In order to identify potential anticancer plants for drug development, this review aims to collect and summarize anticancer activities (in vitro/in vivo) and molecular mechanisms of sub-Saharan African medicinal plant extracts against cancer cell lines. Scientific databases such as ScienceDirect, Google Scholar and PubMed were used to search for research articles published from January 2013 to May 2023 on anticancer medicinal plants in sub-Saharan Africa. The data were analyzed to highlight the cytotoxicity and molecular mechanisms of action of these listed plants. A total of 85 research papers covering 204 medicinal plant species were selected for this review. These plants come from 57 families, the most dominant being the plants of the family Amaryllidaceae (16), Fabaceae (14), Annonaceae (10), Asteraceae (10). Plant extracts exert their anticancer activity mainly by inducing apoptosis and stopping the cell cycle of cancer cells. Several plant extracts from sub-Saharan Africa therefore have strong potential for the search for original anticancer phytochemicals. Chemoproteomics, multi-omics, genetic editing technology (CRISPR/Cas9), combined therapies and artificial intelligence tools are cutting edge emerging technologies that facilitate the discovery and structural understanding of anticancer molecules of medicinal plants, reveal their direct targets, explore their therapeutic uses and molecular bases.

A novel intelligent model for visualized inference of medical diagnosis: A case of TCM.

How to present an intelligent model based on known diagnostic knowledge to assist medical diagnosis and display the reasoning process is an interesting issue worth exploring. This study developed a novel intelligent model for visualized inference of medical diagnosis with a case of Traditional Chinese Medicine (TCM). Four classes of TCM's diagnosis composed of Yin deficiency, Liver Yin deficiency, Kidney Yin deficiency, and Liver-Kidney Yin deficiency were selected as research examples. According to the knowledge of diagnostic points in "Diagnostics of TCM", a total of 2000 samples for training and testing were randomly generated for the four classes of TCM's diagnosis. In addition, a total of 60 clinical samples were collected from hospital clinical cases. Training samples were sent to the pre-training language model of Chinese Bert for training to generate intelligent diagnostic module. Simultaneously, a mathematical algorithm was developed to generate inferential digraphs. In order to evaluate the performance of the model, the values of accuracy, F1 score, Mse, Loss and other indicators were calculated for model training and testing. And the confusion matrices and ROC curves were plotted to estimate the predictive ability of the model. The novel model was also compared with RF and XGBOOST. And some instances of inferential digraphs with the model were displayed and analyzed. It may be a new attempt to solve the problem of interpretable and inferential intelligent models in the field of artificial intelligence on medical diagnosis of TCM.

BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.

Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.