Background: Recent advancements in artificial intelligence (AI) like ChatGPT have expanded possibilities for patient education, yet its impact on perioperative anxiety in total knee arthroplasty (TKA) patients remains unexplored. Methods: In this single-blind, randomized controlled pilot study from April to July 2023, 60 patients were randomly allocated using sealed envelopes to either ChatGPT-assisted or traditional surgeon-led informed consent groups. In the ChatGPT group, physicians used ChatGPT 4.0 to provide standardized, comprehensive responses to patient queries during the consent process, while maintaining their role in interpreting and contextualizing the information. Outcomes were measured using Hospital Anxiety and Depression Scales (HADS), Perioperative Apprehension Scale-7 (PAS-7), Visual Analogue Scales for Anxiety and Pain (VAS-A, VAS-P), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and satisfaction questionnaires. Results: Of 55 patients completing the study, the ChatGPT group showed significantly lower anxiety scores after informed consent (HADS-A: 10.48 ± 3.84 vs 12.75 ± 4.12, P = .04, Power = .67; PAS-7: 12.44 ± 3.70 vs 14.64 ± 2.11, P = .01, Power = .85; VAS-A: 5.40 ± 1.89 vs 6.71 ± 2.27, P = .02, Power = .75) and on the fifth postoperative day (HADS-A: 8.33 ± 3.20 vs 10.71 ± 3.83, P = .01, Power = .79; VAS-A: 3.41 ± 1.58 vs 4.64 ± 1.70, P = .008, Power = .85). The ChatGPT group also reported higher satisfaction with preoperative education (4.22 ± 0.51 vs 3.43 ± 0.84, P<.001, Power = .99) and overall hospitalization experience (4.11 ± 0.65 vs 3.46 ± 0.69, P = .001, Power = .97). No significant differences were found in depression scores, knee function, or pain levels. Conclusions: ChatGPT-assisted informed consent effectively reduced perioperative anxiety and improved patient satisfaction in TKA patients. While these preliminary findings are promising, larger studies are needed to validate these results and explore broader applications of AI in preoperative patient education.

Background: The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China’s DingXiangYuan to offer remote medical services. Objective: This study evaluates ChatGPT-4’s responses against those of professional health care providers in telemedicine, assessing artificial intelligence’s capability to support the surge in remote medical consultations and its impact on health care delivery. Methods: We sourced remote orthopedic consultations from “Doctor DingXiang,” with responses from its certified physicians as the control and ChatGPT’s responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: “logical reasoning,” “internal information,” “external information,” “guiding function,” “therapeutic effect,” “medical knowledge popularization education,” and “overall satisfaction.” We used Fleiss κ to measure agreement among multiple raters. Results: Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT’s “internal information” score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and “therapeutic effect” score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher “logical reasoning” score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), “external information” score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and “guiding function” score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the “medical knowledge popularization education” score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of “overall satisfaction,” the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group’s score points were classified as displaying “fair agreement” (P<.001), and 1 was classified as showing “substantial agreement” (P<.001). In the experimental group, 3 points were classified as indicating “fair agreement,” while 4 suggested “moderate agreement” (P<.001). Conclusions: ChatGPT-4 matches the expertise found in DingXiangYuan forums’ paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.

The incidence of Crohn's disease (CD) and rheumatoid arthritis (RA) co-morbidity, as well as the number of individuals affected, is on the rise due to their shared molecular and cellular factors. This study aimed to identify potential therapeutic targets and medicines for comorbid CD and RA. We integrated single-cell RNA sequencing, Mendelian randomization, and colocalization analysis results from public databases to analyse immune cell subgroups in CD and RA patients and identify candidate therapeutic targets. We further screened potential medicines for the identified candidate targets using the Comparative Toxicogenomics Database (CTD) and molecular docking and molecular dynamics simulations. The proportion of CD8 effector memory T cells (Tem) was consistently elevated in the peripheral blood mononuclear cells (PBMCs) of both CD and RA patients. MYBL1 had a causal effect on the onset of both CD (OR = 1.23; 95 % CI, 1.05–1.45; P = 0.046) and RA (OR = 1.45; 95 % CI, 1.14–1.85; P = 0.04). Four potential therapeutic molecules were retrieved from the CTD database, among which tretinoin (docking score: −6.3 kcal/mol) showed the best potential. Our comprehensive analysis suggests that CD8 Tem cells are a key cell group in comorbid RA and CD and that MYBL1 has a causal effect. Tretinoin was identified as a potential targeted therapeutic drug, which is of great clinical research value.

Background Bone cancer called osteosarcoma (OS), especially its fibroblastic type, makes things very hard in the world of bone diseases. This happens because of its fierce character and the complexity involved in deciding outcomes. Current prognostic models, like the American Joint Committee on Cancer (AJCC) system and Tumor Node Metastasis (TNM) Staging System, don't always fully include important individual patient factors such as age, sex and race. These things are very important for making a correct prediction. Methods A total of 394 patients with fibroblastic osteosarcoma were included in the study, adhering to specified inclusion and exclusion criteria. The cohort was subsequently segregated into training and validation sets at a 7:3 ratio. X-tile software facilitated the determination of optimal age and tumor size cutoffs. Missing data were managed using multiple imputation and K-Nearest Neighbor (KNN) methods. The primary endpoint was cancer-specific survival (CSS), categorized into binary data (survival status at 3 and 5 years) and time-to-event data. Independent prognostic factors were ascertained using the Boruta algorithm, which informed the construction of predictive models employing Cox regression and diverse machine learning algorithms such as Survival Tree, Extra Survival Trees, Random Survival Forest, Gradient Boosting Survival Analysis, Fast Kernel Survival SVM, and Minlip Survival Analysis. Model performance metrics included the concordance index (C-index), accuracy, recall, F1 score, and time-dependent Area Under the Curve (AUC). A calibration plot was generated to validate the accuracy of the most proficient machine learning model. Decision curve analysis (DCA) was implemented to ascertain the model's clinical utility. Additionally, we used the SHapley Additive exPlanations (SHAP) method to show how important our model found key things that can predict outcomes. Results For age, the determined optimal cutoff points were established at 40 and 57 years. Regarding tumor size, these points were set at 60mm and 103mm. Our study identified nine significant independent prognostic factors impacting the cancer-specific survival in patients with fibroblastic osteosarcoma. These included age group, tumor stage, tumor size group, radiation, surgery type, primary site, sex, chemotherapy, and grade group. Comparative analysis of different algorithms, utilizing metrics such as accuracy, recall, F1 score, C-index, and time-dependent AUC, highlighted the Extra Survival Trees model as the superior predictive tool for machine learning. This model demonstrated high efficiency (3-year CSS accuracy: 0.91, 5-year CSS accuracy: 0.89), notable recall rates (3-year: 0.81, 5-year: 0.74), and robust F1 scores (3-year: 0.83, 5-year: 0.80), along with an average AUC of 0.89 and a C-index of 0.92 for training and 0.80 for validation. The calibration curve for this model indicated high predictive accuracy, and its clinical usefulness was further corroborated by decision curve analysis (DCA). SHAP analysis identified 'age group', 'tumor stage', and 'tumor size group' as the three most influential variables impacting cancer-specific survival predictions in fibroblastic osteosarcoma. Our study suggested otherwise than previous ones. It showed that radiation and chemotherapy may not work for treating this type of bone cancer called fibroblastic osteosarcoma. Conclusion Research indicates that predictive analysis using machine learning outperforms traditional methods in forecasting outcomes for patients with fibroblastic osteosarcoma. This development offers considerable promise for enhancing tailored therapeutic approaches and prognostic outcomes in fibroblastic osteosarcoma.

BACKGROUND The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China’s DingXiangYuan to offer remote medical services. OBJECTIVE This study evaluates ChatGPT-4’s responses against those of professional health care providers in telemedicine, assessing artificial intelligence’s capability to support the surge in remote medical consultations and its impact on health care delivery. METHODS We sourced remote orthopedic consultations from “Doctor DingXiang,” with responses from its certified physicians as the control and ChatGPT’s responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: “logical reasoning,” “internal information,” “external information,” “guiding function,” “therapeutic effect,” “medical knowledge popularization education,” and “overall satisfaction.” We used Fleiss κ to measure agreement among multiple raters. RESULTS Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT’s “internal information” score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and “therapeutic effect” score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher “logical reasoning” score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), “external information” score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and “guiding function” score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the “medical knowledge popularization education” score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of “overall satisfaction,” the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group’s score points were classified as displaying “fair agreement” (P<.001), and 1 was classified as showing “substantial agreement” (P<.001). In the experimental group, 3 points were classified as indicating “fair agreement,” while 4 suggested “moderate agreement” (P<.001). CONCLUSIONS ChatGPT-4 matches the expertise found in DingXiangYuan forums’ paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.