Introduction: Large Language Models (LLMs) are sophisticated algorithms that analyze and generate vast amounts of textual data, mimicking human communication. Notable LLMs include GPT-4o by Open AI, Claude 3.5 Sonnet by Anthropic, and Gemini by Google. This scoping review aims to synthesize the current applications and potential uses of LLMs in patient education and engagement.Materials and methods: Following the PRISMA-ScR checklist and methodologies by Arksey, O’Malley, and Levac, we conducted a scoping review. We searched PubMed in June 2024, using keywords and MeSH terms related to LLMs and patient education. Two authors conducted the initial screening, and discrepancies were resolved by consensus. We employed thematic analysis to address our primary research question.Results: The review identified 201 studies, predominantly from the United States (58.2%). Six themes emerged: generating patient education materials, interpreting medical information, providing lifestyle recommendations, supporting customized medication use, offering perioperative care instructions, and optimizing doctor-patient interaction. LLMs were found to provide accurate responses to patient queries, enhance existing educational materials, and translate medical information into patient-friendly language. However, challenges such as readability, accuracy, and potential biases were noted.Discussion: LLMs demonstrate significant potential in patient education and engagement by creating accessible educational materials, interpreting complex medical information, and enhancing communication between patients and healthcare providers. Nonetheless, issues related to the accuracy and readability of LLM-generated content, as well as ethical concerns, require further research and development. Future studies should focus on improving LLMs and ensuring content reliability while addressing ethical considerations.

Large language models (LLMs) have the potential to revolutionize the field of medicine by, among other applications, improving diagnostic accuracy and supporting clinical decision-making. However, the successful integration of LLMs in medicine requires addressing challenges and considerations specific to the medical domain. This viewpoint article provides a comprehensive overview of key aspects for the successful implementation of LLMs in medicine, including transfer learning, domain-specific fine-tuning, domain adaptation, reinforcement learning with expert input, dynamic training, interdisciplinary collaboration, education and training, evaluation metrics, clinical validation, ethical considerations, data privacy, and regulatory frameworks. By adopting a multifaceted approach and fostering interdisciplinary collaboration, LLMs can be developed, validated, and integrated into medical practice responsibly, effectively, and ethically, addressing the needs of various medical disciplines and diverse patient populations. Ultimately, this approach will ensure that LLMs enhance patient care and improve overall health outcomes for all.

Artificial intelligence (AI) and generative language models (GLMs) present significant opportunities for enhancing medical education, including the provision of realistic simulations, digital patients, personalized feedback, evaluation methods, and the elimination of language barriers. These advanced technologies can facilitate immersive learning environments and enhance medical students' educational outcomes. However, ensuring content quality, addressing biases, and managing ethical and legal concerns present obstacles. To mitigate these challenges, it is necessary to evaluate the accuracy and relevance of AI-generated content, address potential biases, and develop guidelines and policies governing the use of AI-generated content in medical education. Collaboration among educators, researchers, and practitioners is essential for developing best practices, guidelines, and transparent AI models that encourage the ethical and responsible use of GLMs and AI in medical education. By sharing information about the data used for training, obstacles encountered, and evaluation methods, developers can increase their credibility and trustworthiness within the medical community. In order to realize the full potential of AI and GLMs in medical education while mitigating potential risks and obstacles, ongoing research and interdisciplinary collaboration are necessary. By collaborating, medical professionals can ensure that these technologies are effectively and responsibly integrated, contributing to enhanced learning experiences and patient care.

Study Design: Delayed diagnosis of degenerative cervical myelopathy (DCM) is likely due to a combination of its subtle symptoms, incomplete neurological assessments by clinicians and a lack of public and professional awareness. Diagnostic criteria for DCM will likely facilitate earlier referral for definitive management. Objectives: This systematic review aims to determine (i) the diagnostic accuracy of various clinical signs and (ii) the association between clinical signs and disease severity in DCM? Methods: A search was performed to identify studies on adult patients that evaluated the diagnostic accuracy of a clinical sign used for diagnosing DCM. Studies were also included if they assessed the association between the presence of a clinical sign and disease severity. The QUADAS-2 tool was used to evaluate the risk of bias of individual studies. Results: This review identified eleven studies that used a control group to evaluate the diagnostic accuracy of various signs. An additional 61 articles reported on the frequency of clinical signs in a cohort of DCM patients. The most sensitive clinical tests for diagnosing DCM were the Tromner and hyperreflexia, whereas the most specific tests were the Babinski, Tromner, clonus and inverted supinator sign. Five studies evaluated the association between the presence of various clinical signs and disease severity. There was no definite association between Hoffmann sign, Babinski sign or hyperreflexia and disease severity. Conclusion: The presence of clinical signs suggesting spinal cord compression should encourage health care professionals to pursue further investigation, such as neuroimaging to either confirm or refute a diagnosis of DCM.

Large Language Models (LLMs) are transforming patient education in medication management by providing accessible information to support healthcare decision-making. Building on our recent scoping review of LLMs in patient education, this perspective examines their specific role in medication guidance. These artificial intelligence (AI)-driven tools can generate comprehensive responses about drug interactions, side effects, and emergency care protocols, potentially enhancing patient autonomy in medication decisions. However, significant challenges exist, including the risk of misinformation and the complexity of providing accurate drug information without access to individual patient data. Safety concerns are particularly acute when patients rely solely on AI-generated advice for self-medication decisions. This perspective analyzes current capabilities, examines critical limitations, and raises questions regarding the possible integration of LLMs in medication guidance. We emphasize the need for regulatory oversight to ensure these tools serve as supplements to, rather than replacements for, professional healthcare guidance.

Study Design: Delayed diagnosis of degenerative cervical myelopathy (DCM) is associated with reduced quality of life and greater disability. Developing diagnostic criteria for DCM has been identified as a top research priority. Objectives: This scoping review aims to address the following questions: What is the diagnostic accuracy and frequency of clinical symptoms in patients with DCM? Methods: A scoping review was conducted using a database of all primary DCM studies published between 2005 and 2020. Studies were included if they (i) assessed the diagnostic accuracy of a symptom using an appropriate control group or (ii) reported the frequency of a symptom in a cohort of DCM patients. Results: This review identified three studies that discussed the diagnostic accuracy of various symptoms and included a control group. An additional 58 reported on the frequency of symptoms in a cohort of patients with DCM. The most frequent and sensitive symptoms in DCM include unspecified paresthesias (86%), hand numbness (82%) and hand paresthesias (79%). Neck and/or shoulder pain was present in 51% of patients with DCM, whereas a minority had back (19%) or lower extremity pain (10%). Bladder dysfunction was uncommon (38%) although more frequent than bowel (23%) and sexual impairment (4%). Gait impairment is also commonly seen in patients with DCM (72%). Conclusion: Patients with DCM present with many different symptoms, most commonly sensorimotor impairment of the upper extremities, pain, bladder dysfunction and gait disturbance. If patients present with a combination of these symptoms, further neuroimaging is indicated to confirm the diagnosis of DCM.