NR1H4 disease: rapidly progressing neonatal intrahepatic cholestasis and early death.

BACKGROUND: Clinical studies on progressive familial intrahepatic cholestasis (PFIC) type 5 caused by mutations in NR1H4 are limited. METHODS: New patients with biallelic NR1H4 variants from our center and all patients from literature were retrospectively analyzed. RESULTS: Three new patients were identified to be carrying five new variants. Liver phenotypes of our patients manifests as low-γ-glutamyl transferase cholestasis, liver failure and related complications. One patient underwent liver transplantation (LT) and survived, and two other patients died without LT. Nine other patients were collected through literature review. Twelve out of 13 patients showed neonatal jaundice, with the median age of onset being 7 days after birth. Reported clinical manifestations included cholestasis (13/13, 100%), elevated AFP (11/11, 100%), coagulopathy (11/11, 100%), hypoglycemia (9/13, 69%), failure to thrive (8/13, 62%), splenomegaly (7/13, 54%), hyperammonemia (7/13, 54%), and hepatomegaly (6/13, 46%). Six of 13 patients received LT at a median age of 6.2 months, and only one patient died of acute infection at one year after LT. Other 7 patients had no LT and died with a median age of 5 months (range 1.2-8). There were 8 patients with homozygous genotype and 5 patients with compound heterozygous genotype. In total, 13 different variants were detected, and 5 out of 12 single or multiple nucleotides variants were located in exon 5. CONCLUSIONS: We identified three newly-diagnosed patients and five novel mutations. NR1H4-related PFIC typically cause progressive disease and early death. LT may be the only lifesaving therapy leading to cure.

Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

OBJECTIVE: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations. MATERIALS AND METHODS: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists. The ChatGPT-generated responses were analyzed statistically in a disease-drug matrix, considering disease-medication associations (Q-value) and expert evaluation. RESULTS: ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. We evaluated its drug suggestions using the Q-value, showing that human expert validation agreement surpassed Q-value cutoff-based agreement. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes. CONCLUSION: ChatGPT provides medication recommendations as a second opinion in dermatology treatment, but its reliability and comprehensiveness need refinement for greater accuracy. In the future, integrating a medical domain-specific knowledge base for training and ongoing optimization will enhance the precision of ChatGPT's results.

Adjustable Cuffless Smartphone Attachment (ACSA+) for Estimation of Blood Pressure Trends: A Pilot Study.

Among the elderly, hypertension remains one of the prevalent health conditions, which requires monitoring and intervention strategies. Nevertheless, regular reporting of blood pressure (BP) from these individuals still poses multiple challenges. However, most people own cell phone and are engaged in phone conversations daily. Here, we propose an adjustable cuffless smartphone attachment (ACSA+) equipped with a PPG sensor for the estimation of BP during phone conversations. ACSA+ can be easily attached to the back of any modern cell phone. ACSA+ will help to continuously collect BP data and store it as a trend line.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

Speech Emotion Recognition Applied to Real-World Medical Consultation.

Since 2020, the COVID-19 epidemic has changed our lives in healthcare behaviors. Forced to wear masks influenced doctor-patient interaction perceptions truly, thus, to build a satisfying relationship is not just empathize with facial expressions. The voice becomes more important for the sake of conquering the burden of masks. Hence, verbal and non-verbal communication will be crucial criteria for doctor-patient interaction during medical consultations and other conversations. In these years, speech emotion recognition has been a popular research domain. In spite of abundant work conducted, nonverbal emotion recognition in medical scenarios is still required to reveal. In this study, we investigate YAMNet transfer learning on Chinese Mandarin speech corpus NTHU-NTUA Chinese Interactive Emotion Corpus (NNIME) and use real-world dermatology clinic recording to test the generalization capability. The results showed that the accuracy validated on NNIME data was 0.59 for activation prediction and 0.57 for valence. Furthermore, the validation accuracy on the doctor-patient dataset was 0.24 for activation and 0.58 for valence, respectively.

Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data.

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another's facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction. This study explores how facial expressions and facial mimicry influence doctor-patient satisfaction during a clinical encounter. We used a facial emotion recognition-based artificial empathy model to analyze 315 recorded clinical video data of doctors and patients in a dermatology outpatient clinic. The results show a significant negative correlation between patients' emotions of sadness and neutral and doctor satisfaction, but no correlation between the duration of doctors mimicking patient emotions and patient satisfaction. These findings provide valuable insights into the future design of systems that can further enhance clinician awareness to maintain communication skills in the search for better doctor-patient satisfaction.

Relationship of Glucagon-like Peptide 1 and Peptide YY with Catch-up Growth in Children Born Small for Gestational Age

Objective: Children born small for gestational age (SGA) are at a greater risk of developing insulin resistance, type 2 diabetes, and cardiovascular disease in adulthood. Gastrointestinal peptides, some secreted by intestinal L cells, regulate glucose and lipid metabolism and act on the hypothalamus to regulate energy homeostasis. The aim of this study was to explore whether gastrointestinal peptides are involved in metabolic disorders in SGA, which remains unclear. Methods: The secretion of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) were investigated in prepubertal children born SGA, the differences between catch-up growth and persistent short stature were compared, and correlation with glucose and lipid metabolism was analyzed. GLP-1, PYY, insulin-like growth factor 1, glucose, insulin, and lipid concentrations were analyzed in prepubertal children aged 4-10 years, stratified into three groups: short-SGA (SGA-s), catch-up growth SGA, and normal growth appropriate for gestational age (AGA). Results: Fasting GLP-1 and PYY concentrations were significantly lower in the SGA group than in the AGA group (p<0.05), and the GLP-1 level in infants born SGA with catch-up growth was lower than that in the SGA-s group (p<0.05). In the SGA population, GLP-1 showed a weak negative correlation with catch-up growth (r=-0.326) and positive correlation with fasting insulin (r=0.331). Conclusion: Lower GLP-1 concentrations may be associated with abnormal glucose metabolism in prepubertal children born SGA with catch-up growth. This is indirect evidence that impaired intestinal L cell function may be involved in the development of metabolic complications in SGA children.

Call for the responsible artificial intelligence in the healthcare.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI's capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions.

The current Objective Structured Clinical Examination (OSCE) is complex, costly, and difficult to provide high-quality assessments. This pilot study employed a focus group and debugging stage to test the Crowdsource Authoring Assessment Tool (CAAT) for the creation and sharing of assessment tools used in editing and customizing, to match specific users' needs, and to provide higher-quality checklists. Competency assessment international experts (n = 50) were asked to 1) participate in and experience the CAAT system when editing their own checklist, 2) edit a urinary catheterization checklist using CAAT, and 3) complete a Technology Acceptance Model (TAM) questionnaire consisting of 14 items to evaluate its four domains. The study occurred between October 2018 and May 2019. The median time for developing a new checklist using the CAAT was 65.76 minutes whereas the traditional method required 167.90 minutes. The CAAT system enabled quicker checklist creation and editing regardless of the experience and native language of participants. Participants also expressed the CAAT enhanced checklist development with 96% of them willing to recommend this tool to others. The use of a crowdsource authoring tool as revealed by this study has efficiently reduced the time to almost a third it would take when using the traditional method. In addition, it allows collaborations to partake on a simple platform which also promotes contributions in checklist creation, editing, and rating.

Incidence and Mortality of Malignant Brain Tumors after 20 Years of Mobile Use.

(1) Objective: This population-based study was performed to examine the trends of incidence and deaths due to malignant neoplasm of the brain (MNB) in association with mobile phone usage for a period of 20 years (January 2000-December 2019) in Taiwan. (2) Methods: Pearson correlation, regression analysis, and joinpoint regression analysis were used to examine the trends of incidence of MNB and deaths due to MNB in association with mobile phone usage. (3) Results: The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB. The compound annual growth rates further support these observations, highlighting consistent growth in mobile phone users and a corresponding increase in MNB incidences and deaths. (4) Conclusions: The results suggest a weaker association between the growing number of mobile phone users and the rising rates of MNB, and no significant correlation was observed between MNB incidences and deaths and mobile phone usage. Ultimately, it is important to acknowledge that conclusive results cannot be drawn at this stage and further investigation is required by considering various other confounding factors and potential risks to obtain more definitive findings and a clearer picture.

Using alert dwell time to filter universal clinical alerts: A machine learning approach.

BACKGROUND: Alerts in computerized physician order entry (CPOE) systems can improve patient safety. However, alerts in rule-based systems cannot be customized based on individual patient or user characteristics. This limitation can lead to the presentation of irrelevant alerts and subsequent alert fatigue. OBJECTIVE: We used machine learning approaches with alert dwell time to filter out irrelevant alerts for physicians based on contextual factors. METHODS: We utilized five machine learning algorithms and a total of 1,120 features grouped into six categories: alert, demographic, environment, diagnosis, prescription, and laboratory results. The output of the models was the alert dwell time within a specified time window to determine the optimal range by the sensitivity analysis. RESULTS: We used 813,026 records (19 categories) from the hospital's outpatient clinic data from 2020 to 2021. The sensitivity analysis showed that a time window with a range of 0.3-4.0 s had the best performance, with an area under the receiver operating characteristic (AUROC) curve of 0.73 and an area under the precision-recall curve (AUPRC) of 0.97. The model built with alert and demographic feature groups showed the best performance, with an AUROC of 0.73. The most significant individual feature groups were alert and demographic, with AUROCs of 0.66 and 0.62, respectively. CONCLUSION: Our study found that alerts and user and patient demographic features are more crucial than clinical features when constructing universal context-aware alerts. Using alert dwell time in combination with a time window is an effective way to determine the trigger status of an alert. The findings of this study can provide useful insights for researchers working on specific and universal context-aware alerts.

New technology for preparing energetic materials by nanofiltration membrane (NF): rapid and efficient preparation of high-purity ammonium dinitramide (ADN).

The development of environment-friendly and non-toxic green energetic materials and their safe, environmentally friendly, and economical production is very important to the national economy and national security. As an innovative, efficient, and environmentally friendly energetic material, the preferred preparation method of ammonium dinitramide (ADN) is the nitro-sulfur mixed acid method, which has the advantages of high yield, simple method, and easy access to raw materials. However, the large number of inorganic salt ions introduced by this method limits the large-scale production of ADN. Nanofiltration (NF) has been widely used in various industrial processes as a separation method with high separation efficiency and simple operation. In this study, NF was used for the desalination and purification of ADN synthesized by the mixed acid method. The effects of NF types, operation process (pressure, temperature, and feed solution concentration) on desalination efficiency, and membrane flux during purification were examined. The results showed that 600D NF could achieve the efficient desalination and purification of ADN. It was verified that the highest desalination and purification efficiency was achieved at 2 MPa pressure, 25 °C, and 1 time dilution of the feed solution, and the membrane flux of the desalination and purification process was stable. Under the optimized process conditions, the removal rate of inorganic salts and other impurities reached 99% (which can be recycled), the purity of ADN reached 99.8%, and the recovery rate reached 99%. This process has the potential for the large-scale production of ADN and provides a new process for the safe, efficient, and cheap preparation of energetic materials.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001-31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results: We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions: The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

Empowering Patients and Transforming Healthcare in the Post-COVID-19 Era: The Role of Digital and Wearable Technologies.

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...].

Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy.

BACKGROUND AND OBJECTIVE: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. METHODS: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. RESULTS: Data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p < = 0.003), including anger, happiness, disgust, and sadness; the only emotion that patients showed more than doctors was surprise (t [326] = -4.428, p < .001) (p < .001). (2) Patients felt happier during the latter half of the session (t [326] = -2.860, p = .005), indicating a good doctor-patient relationship. CONCLUSIONS: Artificial empathy can offer objective observations on how doctors' and patients' emotions change. With the ability to detect emotions in 3/4 view and profile images, artificial empathy could be an accessible evaluation tool to study doctor-patient relationships in practical clinical settings.

Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study.

BACKGROUND: Psoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA. Predicting patients with a risk of PsA is crucial for providing them with early examination and treatment that can prevent irreversible disease progression and function loss. OBJECTIVE: The aim of this study was to develop and validate a prediction model for PsA based on chronological large-scale and multidimensional electronic medical records using a machine learning algorithm. METHODS: This case-control study used Taiwan's National Health Insurance Research Database from January 1, 1999, to December 31, 2013. The original data set was split into training and holdout data sets in an 80:20 ratio. A convolutional neural network was used to develop a prediction model. This model used 2.5-year diagnostic and medical records (inpatient and outpatient) with temporal-sequential information to predict the risk of PsA for a given patient within the next 6 months. The model was developed and cross-validated using the training data and was tested using the holdout data. An occlusion sensitivity analysis was performed to identify the important features of the model. RESULTS: The prediction model included a total of 443 patients with PsA with earlier diagnosis of PsO and 1772 patients with PsO without PsA for the control group. The 6-month PsA risk prediction model that uses sequential diagnostic and drug prescription information as a temporal phenomic map yielded an area under the receiver operating characteristic curve of 0.70 (95% CI 0.559-0.833), a mean sensitivity of 0.80 (SD 0.11), a mean specificity of 0.60 (SD 0.04), and a mean negative predictive value of 0.93 (SD 0.04). CONCLUSIONS: The findings of this study suggest that the risk prediction model can identify patients with PsO at a high risk of PsA. This model may help health care professionals to prioritize treatment for target high-risk populations and prevent irreversible disease progression and functional loss.

Synthesis and characterization of potential polycyclic energetic materials using bicyclic triazole and azetidine structures as building blocks.

Exploring the design strategy of new energetic materials is crucial to promote the development of energetic materials. In this study, a method for designing polycyclic energetic materials is proposed by combining the azetidine structure with azobis-1,2,4-triazole or bi-1,2,4-triazole. A series of typical triazolyl polycyclic compounds were designed and synthesized by simple nucleophilic reaction, which included 5,5'-dichloro-3,3'-bis(3,3'-difluoroazetidine)-4,4'-azobis-1,2,4-triazole (1), 5,5'-dichloro-3,3'-bis(3,3'-difluoroazetidine)-4,4'-bi-1,2,4-triazole (2), 5,5'-dichloro-3-(N,N-dimethyl)-3'-(3,3'-difluoroazetidine)-4,4'-bi-1,2,4-triazole (3) 5,5'-dichloro-3,3'-bis(3,3'-dinitroazetidine)-4,4'-bi-1,2,4-triazole (4), 5,5'-dichloro-3-(N,N-dimethyl)-3'-(3,3'-dinitroazetidine)-4,4'-bi-1,2,4-triazole (5), and 5,5'-diazido-3,3'-bis(3,3'-difluoroazetidine)-4,4'-azo-1,2,4-triazole (6). These designed and synthesized polycyclic compounds (1, 2, 3) have high decomposition temperatures (>200 °C). The molecular van der Waals surface electrostatic potentials suggested the reactivity of compounds 1, 2, and 3 when attacked by nucleophiles. The natural bond orbital and Hirshfeld surface analysis proved the essential reason for the stability of these compounds in theory. The formula design example suggests that some triazolyl polycyclic compounds (4, 5, and 6) are potentially explosives, suggesting that this strategy is feasible for constructing the triazolyl polycyclic energetic compounds.

Artificial intelligence in diabetic retinopathy: Bibliometric analysis.

BACKGROUND: The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area. AIMS: We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area. METHODS: Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references. RESULTS: In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers. CONCLUSION: This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.