Objective: The rapid expansion of digital healthcare has heightened the volume of patient communication, thereby increasing the workload for healthcare professionals. Large Language Models (LLMs) hold promises for offering automated responses to patient questions relayed through eHealth platforms, yet concerns persist regarding their effectiveness, accuracy, and limitations in healthcare settings. This study aims to evaluate the current evidence on the performance and perceived suitability of LLMs in healthcare, focusing on their role in supporting clinical decision-making and patient communication. Materials and methods: A systematic search in PubMed and Embase up to June 11, 2025 identified 330 studies, of which 20 met the inclusion criteria for comparing the accuracy and adequacy of medical information provided by LLMs versus healthcare professionals and guidelines. The search strategy combined terms related to LLMs, healthcare professionals, and patient questions. The ROBINS-I tool assessed the risk of bias. Results: A total of nineteen studies focused on medical specialties and one on the primary care setting. Twelve studies favored the responses generated by LLMs, six reported mixed results, and two favored the healthcare professionals’ response. Bias components generally scored moderate to low, indicating a low risk of bias. Discussion and conclusions: The review summarizes current evidence on the accuracy and adequacy of medical information provided by LLMs in response to patient questions, compared to healthcare professionals and clinical guidelines. While LLMs show potential as supportive tools in healthcare, their integration should be approached cautiously due to inconsistent performance and possible risks. Further research is essential before widespread adoption.

Background: The number of patients suffering from knee complaints is increasing, leading to increased orthopedic healthcare consumption. Predicting knee diagnoses prior to consultation may be valuable in optimizing the consultation workflow. Therefore, the purpose of this study was to develop and internally validate a machine learning (ML) algorithm for predicting a knee diagnosis group for patients aged 18 years and older, based on computer-assisted history taking. Methods: A prospective cohort study at a single general district hospital was conducted to identify patients referred to an orthopedic surgeon for knee complaints. In total, 1172 patients were included, with an average age of 54 years (interquartile range 36–66), of which the majority were female (n = 594, 50.7%). The most frequent diagnosis group was knee osteoarthritis (n = 775, 66.1%), followed by ligamentous injuries (n = 208, 17.7%) and otherwise classified (n = 189, 16.1%). First, the dataset was randomly split 80:20 into training and test subsets. Then, a random forest algorithm was used to identify the variables predictive of a knee diagnosis group. Five different ML algorithms were developed, internally validated, and assessed by discrimination (area under the receiver operating characteristic curve, AUC), accuracy, precision (positive predictive value), recall (sensitivity), and F1‑score (the harmonic mean of precision and recall). Results: The models included patient characteristics and computer-assisted history taking. The support vector machine algorithm had the best performance for knee diagnosis group prediction, with good discrimination (area under the receiver operating characteristic curve, AUC = 0.92), accuracy (0.84), precision (0.85), recall (0.84) and F1-score (0.82). Conclusions: The developed ML algorithm shows promise in predicting a knee diagnosis group in patients presenting with knee complaints to an orthopedic practice. Integrating this algorithm could streamline the consultation workflow by directing patients predicted to have knee osteoarthritis to orthopedic surgeons specializing in knee osteoarthritis, and those predicted to have ligamentous injuries to orthopedic surgeons specializing in sports and traumatic injuries.