Title: Can ChatGPT 4.0 Reliably Answer Patient Frequently Asked Questions About Boxer’s Fractures?
ChatGPT 4.0 received a B grade for answering FAQs on boxer's fractures. 🤖💬 Minor clarifications needed.
AI Identifies Long COVID Care Requirements in Hospital Patients
AI identifies long COVID care needs in hospitals, highlighting patient sub-populations for tailored treatment. 🤖🏥
Using voice recognition and machine learning techniques for detecting patient-reported outcomes from conversational voice in palliative care patients.
Voice recognition in palliative care shows promise for capturing patient-reported outcomes. 📊 Further research needed for improved accuracy. 🗣️
PocDoc Launches Cardiovascular Disease Screening App in West Yorkshire
PocDoc introduces a cardiovascular disease screening app in West Yorkshire, enhancing access to heart health assessments. ❤️🩺
From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning.
Recent research highlights the potential of using human nails to detect fentanyl. 🧪🔍 Machine learning enhances accuracy in toxicology.
Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.
Medical history can predict disease onset and aid in responding to health threats like COVID-19. 📊🩺
Assessment of decision-making with locally run and web-based large language models versus human board recommendations in otorhinolaryngology, head and neck surgery.
Exploring LLMs in cancer treatment decisions 🧠💉: A study compares human recommendations with AI models in otorhinolaryngology.
Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.
Qwen-2.5 excels in nursing exams! 📚🤖 Study shows it outperforms other models, enhancing medical education accuracy.
Insights on Healthcare Trends for 2025
Healthcare trends for 2025 include AI, cybersecurity, digital therapeutics, and telehealth advancements. 📈💻🩺
Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.
Machine learning shows promise in predicting urinary tract infections. Key factors include urine and blood test results, age, and gender. 🤖💧