The copilot framework, called Healthcare Copilot, enhances language models for medical consultations. It has three components for patient interactions, memory, and dialogue processing. It significantly improves inquiry capability, fluency, accuracy, and safety in medical conversations.

Scientists developed a specialized language model called ChatDoctor to improve the accuracy of medical advice given by large language models. By refining the model using real-world patient-doctor interactions and allowing it to access reliable online and offline sources, ChatDoctor significantly improved its understanding of patient inquiries and ability to provide accurate advice.

GPT-4 demonstrates comparable medical accuracy to human experts in providing medical advice for cardiology. However, human experts outperformed GPT-4 in specific categories, highlighting limitations in clinical judgment. Further research and improvements in language models are necessary for safe and effective use in medical consultations.

The study assesses the effectiveness of language models in responding to inquiries from autistic individuals in a Chinese setting. The results show that while physicians' responses were superior overall, language models can provide valuable guidance and demonstrate empathy. Further research and optimization are needed for integration in clinical settings.

DISC-MedLLM is a solution that uses Large Language Models to provide accurate medical responses in conversational healthcare services. It surpasses existing models by using high-quality datasets and demonstrates effectiveness in bridging the gap between general language models and medical consultation.

LLM-AMT is a system designed to enhance the proficiency of language models in the medical field. By incorporating authoritative medical textbooks, LLM-AMT improves response quality and outperforms specialized models trained on medical corpus.

Language models have been successful in interacting with humans but often make incorrect judgments due to hallucinations. This paper introduces an evaluation framework to assess language models as virtual doctors, with a focus on their ability to make accurate diagnoses. Fine-tuning with a medical consultation training set improves the models' performance and reduces hallucinations.

Large language models (LLMs) trained by deep learning algorithms can generate text responses to user prompts, leading to potential use in clinical consultation. However, limitations like confabulations, lack of contextual awareness, and biases prevent safe clinical deployment. Infectious diseases clinicians should engage with LLMs to advocate for responsible use in specialist care.

Large language models like ChatGPT are increasingly being used as information sources in medicine, but a study found that their performance in answering clinical case-based questions in otorhinolaryngology (ORL) was inferior to ORL consultants. Although ChatGPT provided longer and more coherent answers, medical adequacy and conciseness were significantly lower compared to ORL consultants' answers.

NLP in healthcare is usually focused on patient-centered services, but the potential for NLP to assist inexperienced doctors in communication skills is largely unexplored. "ChatCoach" is a human-AI framework that allows medical learners to practice dialogues with a patient agent and receive structured feedback from a coach agent, improving their skills.

The development of large language models like ChatGPT raises concerns in various domains, including healthcare and bioethics. These concerns include issues of informed consent, the risk of medical deepfakes and misinformation, competition law, user challenges, algorithmic monoculture, and environmental impact. However, an analysis of ChatGPT's impact on bioethics should also consider how it affects the interpersonal, critical, and reason-giving aspect of healthcare.

The study compared the use of a digital health app along with standard care to standard care alone for patients undergoing ACL surgery. The results showed that using the app in addition to standard care significantly improved pain and symptoms before and after surgery. The app is considered a safe and effective tool for prehabilitation and rehabilitation in ACL surgery.

Hospitalized psychiatric patients perceive medical consultations as important in bridging the gap between psychiatric and somatic treatment, providing a sense of security and acknowledgment. However, memory impairment and psychiatric treatment hindered the full utilization of these consultations. Support from psychiatric staff is crucial for initiating somatic interventions and improving patient health.