Despite recent advances, the adoption of computer vision methods into clinical and commercial applications has been hampered by the limited availability of accurate ground truth tissue annotations required to train robust supervised models. Generating such ground truth can be accelerated by annotating tissue molecularly using immunofluorescence staining (IF) and mapping these annotations to a post-IF H&E (terminal H&E). Mapping the annotations between the IF and the terminal H&E increases both the scale and accuracy by which ground truth could be generated. However, discrepancies between terminal H&E and conventional H&E caused by IF tissue processing have limited this implementation. We sought to overcome this challenge and achieve compatibility between these parallel modalities using synthetic image generation, in which a cycle-consistent generative adversarial network (CycleGAN) was applied to transfer the appearance of conventional H&E such that it emulates the terminal H&E. These synthetic emulations allowed us to train a deep learning (DL) model for the segmentation of epithelium in the terminal H&E that could be validated against the IF staining of epithelial-based cytokeratins. The combination of this segmentation model with the CycleGAN stain transfer model enabled performative epithelium segmentation in conventional H&E images. The approach demonstrates that the training of accurate segmentation models for the breadth of conventional H&E data can be executed free of human-expert annotations by leveraging molecular annotation strategies such as IF, so long as the tissue impacts of the molecular annotation protocol are captured by generative models that can be deployed prior to the segmentation process.

BACKGROUND: Those receiving the diagnosis of multiple sclerosis (MS) over the next ten years will predominantly be part of Generation Z (Gen Z). Recent observations within our clinic suggest that younger people with MS utilize online generative artificial intelligence (AI) platforms for personalized medical advice prior to their first visit with a specialist in neuroimmunology. The use of such platforms is anticipated to increase given the technology driven nature, desire for instant communication, and cost-conscious nature of Gen Z. Our objective was to determine if ChatGPT (Generative Pre-trained Transformer) could diagnose MS in individuals earlier than their clinical timeline, and to assess if the accuracy differed based on age, sex, and race/ethnicity.
METHODS: People with MS between 18 and 59 years of age were studied. The clinical timeline for people diagnosed with MS was retrospectively identified and simulated using ChatGPT-3.5 (GPT-3.5). Chats were conducted using both actual and derivatives of their age, sex, and race/ethnicity to test diagnostic accuracy. A Kaplan-Meier survival curve was estimated for time to diagnosis, clustered by subject. The p-value testing for differences in time to diagnosis was accomplished using a general Wilcoxon test. Logistic regression (subject-specific intercept) was used to capture intra-subject correlation to test the accuracy prior to and after the inclusion of MRI data.
RESULTS: The study cohort included 100 unique people with MS. Of those, 50 were members of Gen Z (38 female; 22 White; mean age at first symptom was 20.6 years (y) (standard deviation (SD)=2.2y)), and 50 were non-Gen Z (34 female; 27 White; mean age at first symptom was 37.0y (SD=10.4y)). In addition, a total of 529 people that represented digital simulations of the original cohort of 100 people (333 female; 166 White; 136 Black/African American; 107 Asian; 120 Hispanic, mean age at first symptom was 31.6y (SD=12.4y)) were generated allowing for 629 scripted conversations to be analyzed. The estimated median time to diagnosis in clinic was significantly longer at 0.35y (95% CI=[0.28, 0.48]) versus that by ChatGPT at 0.08y (95% CI=[0.04, 0.24]) (p<0.0001). There was no difference in the diagnostic accuracy between ages and by race/ethnicity prior to the inclusion of MRI data. However, prior to including the MRI data, males had a 47% less likely chance of a correct diagnosis relative to females (p=0.05). Post-MRI data inclusion within GPT-3.5, the odds of an accurate diagnosis was 4.0-fold greater for Gen Z participants, relative to non-Gen Z participants (p=0.01) with the diagnostic accuracy being 68% less in males relative to females (p=0.009), and 75% less for White subjects, relative to non-White subjects (p=0.0004).
CONCLUSIONS: Although generative AI platforms enable rapid information access and are not principally designed for use in healthcare, an increase in use by Gen Z is anticipated. However, the obtained responses may not be generalizable to all users and bias may exist in select groups.

BACKGROUND: Evaluating the accuracy and educational utility of artificial intelligence-generated medical cases, especially those produced by large language models such as ChatGPT-4 (developed by OpenAI), is crucial yet underexplored.
OBJECTIVE: This study aimed to assess the educational utility of ChatGPT-4-generated clinical vignettes and their applicability in educational settings.
METHODS: Using a convergent mixed methods design, a web-based survey was conducted from January 8 to 28, 2024, to evaluate 18 medical cases generated by ChatGPT-4 in Japanese. In the survey, 6 main question items were used to evaluate the quality of the generated clinical vignettes and their educational utility, which are information quality, information accuracy, educational usefulness, clinical match, terminology accuracy (TA), and diagnosis difficulty. Feedback was solicited from physicians specializing in general internal medicine or general medicine and experienced in medical education. Chi-square and Mann-Whitney U tests were performed to identify differences among cases, and linear regression was used to examine trends associated with physicians\' experience. Thematic analysis of qualitative feedback was performed to identify areas for improvement and confirm the educational utility of the cases.
RESULTS: Of the 73 invited participants, 71 (97%) responded. The respondents, primarily male (64/71, 90%), spanned a broad range of practice years (from 1976 to 2017) and represented diverse hospital sizes throughout Japan. The majority deemed the information quality (mean 0.77, 95% CI 0.75-0.79) and information accuracy (mean 0.68, 95% CI 0.65-0.71) to be satisfactory, with these responses being based on binary data. The average scores assigned were 3.55 (95% CI 3.49-3.60) for educational usefulness, 3.70 (95% CI 3.65-3.75) for clinical match, 3.49 (95% CI 3.44-3.55) for TA, and 2.34 (95% CI 2.28-2.40) for diagnosis difficulty, based on a 5-point Likert scale. Statistical analysis showed significant variability in content quality and relevance across the cases (P<.001 after Bonferroni correction). Participants suggested improvements in generating physical findings, using natural language, and enhancing medical TA. The thematic analysis highlighted the need for clearer documentation, clinical information consistency, content relevance, and patient-centered case presentations.
CONCLUSIONS: ChatGPT-4-generated medical cases written in Japanese possess considerable potential as resources in medical education, with recognized adequacy in quality and accuracy. Nevertheless, there is a notable need for enhancements in the precision and realism of case details. This study emphasizes ChatGPT-4\'s value as an adjunctive educational tool in the medical field, requiring expert oversight for optimal application.

OBJECTIVE: To explore the integration of generative AI, specifically large language models (LLMs), in ophthalmology education and practice, addressing their applications, benefits, challenges, and future directions.
METHODS: A literature review and analysis of current AI applications and educational programs in ophthalmology.
METHODS: Analysis of published studies, reviews, articles, websites, and institutional reports on AI use in ophthalmology. Examination of educational programs incorporating AI, including curriculum frameworks, training methodologies, and evaluations of AI performance on medical examinations and clinical case studies.
RESULTS: Generative AI, particularly LLMs, shows potential to improve diagnostic accuracy and patient care in ophthalmology. Applications include aiding in patient, physician, and medical students\' education. However, challenges such as AI hallucinations, biases, lack of interpretability, and outdated training data limit clinical deployment. Studies revealed varying levels of accuracy of LLMs on ophthalmology board exam questions, underscoring the need for more reliable AI integration. Several educational programs nationwide provide AI and data science training relevant to clinical medicine and ophthalmology.
CONCLUSIONS: Generative AI and LLMs offer promising advancements in ophthalmology education and practice. Addressing challenges through comprehensive curricula that include fundamental AI principles, ethical guidelines, and updated, unbiased training data is crucial. Future directions include developing clinically relevant evaluation metrics, implementing hybrid models with human oversight, leveraging image-rich data, and benchmarking AI performance against ophthalmologists. Robust policies on data privacy, security, and transparency are essential for fostering a safe and ethical environment for AI applications in ophthalmology.

Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI.

UNASSIGNED: Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs\' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.

Demographics, social determinants of health, and family history documented in the unstructured text within the electronic health records are increasingly being studied to understand how this information can be utilized with the structured data to improve healthcare outcomes. After the GPT models were released, many studies have applied GPT models to extract this information from the narrative clinical notes. Different from the existing work, our research focuses on investigating the zero-shot learning on extracting this information together by providing minimum information to the GPT model. We utilize de-identified real-world clinical notes annotated for demographics, various social determinants, and family history information. Given that the GPT model might provide text different from the text in the original data, we explore two sets of evaluation metrics, including the traditional NER evaluation metrics and semantic similarity evaluation metrics, to completely understand the performance. Our results show that the GPT-3.5 method achieved an average of 0.975 F1 on demographics extraction, 0.615 F1 on social determinants extraction, and 0.722 F1 on family history extraction. We believe these results can be further improved through model fine-tuning or few-shots learning. Through the case studies, we also identified the limitations of the GPT models, which need to be addressed in future research.

UNASSIGNED: The purpose of this study is to explore student perceptions of generative AI use and cheating in health professions education. The authors sought to understand how students believe generative AI is acceptable to use in coursework.
UNASSIGNED: Five faculty members surveyed students across health professions graduate programs using an updated, validated survey instrument. Students anonymously completed the survey online, which took 10-20 min. Data were then tabulated and reported in aggregate form.
UNASSIGNED: Nearly 400 students from twelve academic programs including health and rehabilitation science, occupational therapy, physical therapy, physician assistant studies, speech-language pathology, health administration and health informatics, undergraduate healthcare studies, nurse anesthesiology, and cardiovascular perfusion. The majority of students identify the threat of generative AI to graded assignments such as tests and papers, but many believe it is acceptable to use these tools to learn and study outside of graded assignments.
UNASSIGNED: Generative AI tools provide new options for students to study and learn. Graduate students in the health professions are currently using generative AI applications but are not universally aware or in agreement of how its use threatens academic integrity. Faculty should provide specific guidance on how generative AI applications may be used.

暂无摘要。

Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdiagnosed. As machine learning technologies have been used to aid diagnostics in the past, this study aims to test ChatGPT\'s suitability for rare disease diagnostic support with the enhancement provided by Retrieval Augmented Generation (RAG). RareDxGPT, our enhanced ChatGPT model, supplies ChatGPT with information about 717 rare diseases from an external knowledge resource, the RareDis Corpus, through RAG. In RareDxGPT, when a query is entered, the three documents most relevant to the query in the RareDis Corpus are retrieved. Along with the query, they are returned to ChatGPT to provide a diagnosis. Additionally, phenotypes for thirty different diseases were extracted from free text from PubMed\'s Case Reports. They were each entered with three different prompt types: \"prompt\", \"prompt + explanation\" and \"prompt + role play.\" The accuracy of ChatGPT and RareDxGPT with each prompt was then measured. With \"Prompt\", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 37 % of the cases correct. With \"Prompt + Explanation\", RareDxGPT had a 43 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. With \"Prompt + Role Play\", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. To conclude, ChatGPT, especially when supplying extra domain specific knowledge, demonstrates early potential for rare disease diagnosis with adjustments.