Erik Widman, Ph.D.

👉🏼 Prompt engineering on leveraging large language models in generating response to InBasket messages 🤓 Sherry Yan 👇🏻 https://lnkd.in/eZpReUeH 🔍 Focus on data insights: - 📈 The study demonstrated a significant increase in physician acceptance of LLM-generated draft responses, rising from 62% to 84% after three iterations of prompt engineering. - 🗣️ Patients reported a notable improvement in the tone (78%) and overall quality (80%) of messages generated by the optimized prompts compared to the original ones. - 🤖 A majority of patients (76%) could not distinguish between human and LLM-generated responses, indicating high-quality output from the model. 💡 Main outcomes and implications: - 🔄 Iterative prompt engineering proved effective in enhancing the quality of responses to Patient Medical Advice Requests (PMARs), suggesting a viable method for improving clinician-patient communication. - 🧠 Clinicians expressed that the use of LLMs could significantly reduce cognitive load when managing InBasket messages, potentially leading to improved efficiency in clinical settings. - 💬 The findings highlight the potential for LLMs to bridge gaps in patient care by providing timely and relevant information, thus improving patient satisfaction. 📚 Field significance: - 🌐 This research underscores the transformative role of AI and LLMs in healthcare, particularly in managing communication and administrative tasks. - 🏥 The study contributes to the growing body of evidence supporting the integration of AI tools in clinical workflows, paving the way for future innovations in patient care. - 📊 It emphasizes the importance of user feedback in refining AI applications, ensuring that they meet the needs of both clinicians and patients effectively. 🗄️: [#AI #LargeLanguageModels #Healthcare #PatientCare #PromptEngineering #ClinicalCommunication #CognitiveLoad #PatientSatisfaction #InBasketMessages]

👉🏼 Fine-Tuned Bidirectional Encoder Representations From Transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study 🤓 Eunbeen Jo 👇🏻 https://lnkd.in/gx9UH89R 🔍 Focus on data insights: - 📊 ChatGPT demonstrated a 6.2% answer avoidance rate, indicating areas for improvement in responsiveness. - ⚖️ The fine-tuned KM-BERT model achieved an accuracy of 97.7%, highlighting its superior performance in medical specialty recommendations compared to ChatGPT's 93.9%. - 🚦 Precision and recall metrics indicate KM-BERT's effectiveness in correctly identifying relevant specialties, with precision at 57.0% and recall at 65.2%, whereas ChatGPT struggled with lower figures

👉🏼 Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model 🤓 Daisuke Ono 👇🏻 https://lnkd.in/e2RK6-xr 🔍 Focus on data insights: - GPT-4Vision accurately recognized staining techniques and tissue origin but struggled with specific lesion identification. - The interpretation of images was notably influenced by the provided textual context, sometimes leading to diagnostic inaccuracies. - Few-shot learning significantly improved GPT-4Vision's diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning. 💡 Main outcomes and implications: - GPT-4Vision faces challenges in independently interpreting histopathological images. - Few-shot learning is a promising approach to enhance GPT-4Vision's performance, especially in neuropathology where acquiring extensive labeled datasets is challenging. 📚 Field significance: - Artificial intelligence advancements like GPT-4Vision have expanded the potential for medical image interpretation. - Comparative analysis with traditional convolutional neural network models provides insights into improving diagnostic accuracy in neurodegenerative disease histopathology. 🗄️: #neurodegenerative #histopathology #GPT-4Vision #fewshotlearning

👉🏼 Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems 🤓 Syed Ali Haider 👇🏻 https://lnkd.in/eb3hb8se 🔍 Focus on data insights: - 📊 Gemini achieved 98% accuracy in breast classification, outperforming ChatGPT-4's 71%. - 🧠 Both models excelled in specific classifications, indicating strengths in certain areas of medical diagnostics. - 🔬 The study utilized fifty clinical scenarios to rigorously assess LLM performance across five established classification systems. 💡 Main outcomes and implications: - 🚀 The superior performance of Gemini suggests its potential as a valuable tool for plastic surgeons in diagnostic processes. - 🏥 Enhanced accuracy in classification can lead to better-informed treatment decisions, ultimately improving patient outcomes. - 🔄 Integration of LLMs into clinical practice may revolutionize diagnostic support in the field of plastic surgery. 📚 Field significance: - 🌟 This research highlights the growing role of artificial intelligence in medicine, particularly in enhancing diagnostic accuracy. - 🔗 The findings could pave the way for further studies on LLM applications in various medical specialties. - 📈 As LLM technology evolves, its integration into healthcare practices may significantly impact patient care and surgical outcomes. 🗄️: [#diagnostics #artificialintelligence #machinelearning #plasticurgery #breastclassification #medicalresearch #patientoutcomes #healthcareinnovation]

👉🏼 Unveiling the e-Servicescape of ChatGPT: Exploring User Psychology and Engagement in AI-Powered Chatbot Experiences 🤓 Minseong Kim 👇🏻 https://lnkd.in/eZZeTE-Y 🔍 Focus on data insights: - 📊 The study utilized structural equation modeling to analyze user responses, revealing significant correlations between e-servicescape dimensions and emotional states. - 🧠 Negative emotions were notably influenced by factors such as security and visual appeal, while positive emotions were linked to customization and interactivity. - 📈 User satisfaction was found to be a critical mediator between emotional responses and the intention to engage with ChatGPT. 💡 Main outcomes and implications: - 🌐 The findings highlight the importance of e-servicescapes in shaping user experiences with AI technologies, suggesting that businesses should prioritize these elements to enhance user engagement. - 🔄 Understanding the emotional drivers can help marketers tailor their strategies to improve user satisfaction and retention in digital environments. - 💬 The research extends the concept of environmental psychology into the realm of AI, providing a framework for future studies on user interaction with digital services. 📚 Field significance: - 📖 This study contributes to the growing body of literature on digital environmental psychology, emphasizing the role of emotional states in technology adoption. - 🔍 It offers valuable insights for both scholars and practitioners in the fields of marketing and user experience design, particularly in the context of AI-driven services. - 🌟 The implications of this research can inform the development of more effective AI interfaces that cater to user emotional needs. 🗄️: [#eServicescape #UserExperience #EmotionalDesign #AI #Chatbots #DigitalPsychology #MarketingInsights #UserEngagement]

👉🏼 Pre-trained Language Models in Medicine: A Survey 🤓 Xudong Luo https://lnkd.in/eubyPuA5 🔍 Focus on data insights: - Pre-trained Language Models (PLMs) like BERT, BioBERT, and ChatGPT have shown potential in medical NLP tasks. - Survey covers achievements and methodologies of applying PLMs in medical NLP tasks. 💡 Main outcomes and implications: - Overview of basic concepts, methodologies, advantages, and steps of applying PLMs in medical NLP tasks. - Summary of recent research findings, strengths vs weaknesses, and potential limitations. 📚 Field significance: - Enhancing models' reliability, explainability, and fairness for promoting PLMs in clinical practice. 🗄️: #NaturalLanguageProcessing #PretrainedModels #MedicalNLP #DataInsights

👉🏼 A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare 🤓 Leyao Wang 👇🏻 https://lnkd.in/e--t65tY 🔍 Focus on data insights: - ChatGPT from OpenAI was the most popular conversational LLM in healthcare applications. - Concerns about reliability and bias were prevalent in research papers utilizing LLMs. - LLMs showed promising results in summarization and providing medical knowledge with high accuracy. 💡 Main outcomes and implications: - Future studies should prioritize improving the reliability of LLM applications in complex health-related tasks. - Investigation into how LLM applications introduce bias and privacy issues is crucial for further development. - Legal, social, and technical efforts are necessary to address concerns and enhance the application of LLMs in healthcare. 📚 Field significance: - Healthcare applications - Conversational Large Language Models - Data insights 🗄️: [#healthcare #LLMs #datainsights]

👉🏼 Understanding the Impact of Large Language Models in Healthcare Communication 🤓 Zefeng Yang 👇🏻 https://lnkd.in/eAtYVexV 🔍 Focus on data insights: - 📊 Large language models (LLMs) demonstrate advanced capabilities in natural language comprehension and generation, enhancing communication in healthcare settings. - 🧠 The evolution of LLMs parallels significant advancements in deep learning, particularly through transformer architecture, which improves their functionality. - 🔄 With extensive training data, LLMs can retain memory and comprehend complex medical information, facilitating better interactions between clinicians and patients. 💡 Main outcomes and implications: - 📝 LLMs can automate medical documentation, potentially reducing administrative burdens on healthcare providers. - 🤖 Future applications may include autonomous diagnosis and treatment suggestions, improving efficiency in patient care. - 📚 LLMs can assist in triage, summarizing medical documents, and personalizing patient education materials, thereby enhancing patient understanding and engagement. 📚 Field significance: - 🌐 The integration of LLMs in healthcare could revolutionize communication, leading to improved patient outcomes and satisfaction. - 🔍 Ongoing research is necessary to address limitations and ensure the safe and effective use of LLMs in real-world clinical settings. - 🚀 As technology advances, LLMs are poised to play a critical role in the future of healthcare delivery, particularly in specialized fields like ophthalmology. 🗄️: [#LargeLanguageModels #HealthcareCommunication #NaturalLanguageProcessing #DeepLearning #PatientCare #MedicalDocumentation #Ophthalmology #AIinHealthcare]

👉🏼 An Autonomous AI Agent for Streamlined Multi-Omic Bioinformatics 🤓 Juexiao Zhou 👇🏻 https://lnkd.in/egsngA94 🔍 Focus on data insights: - 📊 AutoBA leverages large language models

👉🏼 Application of ChatGPT to Support Nutritional Recommendations for Dialysis Patients - A Qualitative and Quantitative Evaluation 🤓 Lin-Chun Wang 👇🏻 https://lnkd.in/et9eMT2R 🔍 Focus on data insights: - 📊 ChatGPT-4 generated a daily meal plan that was evaluated for nutritional accuracy. - 🥗 The renal dietitian rated the quality of recipes and cooking instructions, revealing strengths and weaknesses in AI-generated content. - 💻 The study compared ChatGPT’s nutritional analysis to USDA-approved software, highlighting significant underestimations in nutrient values. 💡 Main outcomes and implications: - 🔍 ChatGPT's nutritional analysis exhibited major inaccuracies, notably underestimating calories and essential nutrients by substantial margins. - 📉 This suggests a need for enhanced algorithms or oversight when utilizing AI for medical nutritional advice. - 🌍 The ability to translate nutritional guidance into multiple languages speaks to improving accessibility, although translation reliability was noted. 📚 Field significance: - 🚑 This study emphasizes the critical role of qualitative assessments of AI-generated content in healthcare settings. - 🤖 It underscores the potential of AI tools to support personalized dietary recommendations but stresses the necessity for rigorous evaluation before clinical application. - ⚕️ Findings may influence future research and development in AI-based tools for dietary management, particularly in specialized patient populations like dialysis patients. 🗄️: [#ChatGPT #Dialysis #Nutrition #AI #MedicalApplications #PersonalizedCare #NutritionalAnalysis #HealthTech]

👉🏼 Evaluating the Performance of ChatGPT in Answering Questions on Carbon Monoxide Poisoning 🤓 Jun Qiu 👇🏻 https://lnkd.in/ebbZPijy 🔍 Focus on data insights: - 📊 Approximately 50% of ChatGPT's responses showed high consistency, indicating moderate reliability in its outputs. - ❓ One-third of the responses displayed significant inaccuracies or vague answers, signaling areas in need of improvement. - 🌐 The accuracy rates were 61.1% in Chinese and 57% in English, highlighting a disparity between language performance. 💡 Main outcomes and implications: - 🔧 The findings suggest that while ChatGPT can provide useful insights, its current version requires enhancements in both consistency and accuracy. - 🩺 This study emphasizes the necessity for continued human oversight in clinical settings, as AI-generated responses may not always be dependable for critical healthcare decisions. - 🚀 Future developments should aim to improve data handling capabilities and accuracy metrics for AI tools used in the medical field. 📚 Field significance: - 🧠 Understanding AI's role in medicine is crucial as it becomes more integrated into healthcare, necessitating studies like this to gauge effectiveness. - 🔍 The research contributes to ongoing conversations about the potential and limitations of AI technology in real-world applications, particularly in sensitive areas like healthcare. 🗄️: [#AI #Healthcare #ChatGPT #CarbonMonoxidePoisoning #MedicalReliability #DataInsights #AIInMedicine #ClinicalDecisions]

👉🏼 Evaluating the Appropriateness, Consistency, and Readability of ChatGPT in Critical Care Recommendations 🤓 Kaan Y Balta 👇🏻 https://lnkd.in/eTiUVmGx 🔍 Focus on data insights: - 📊 ChatGPT 4.0 demonstrated significantly higher appropriateness scores compared to ChatGPT 3.5, indicating improvements in generating relevant clinical recommendations. - 🔄 Both models exhibited variability in consistency, with ChatGPT 4.0 showing a 40% consistency rate versus 28% for ChatGPT 3.5, suggesting room for improvement in reliability. - 📚 Readability levels were similar across both versions, highlighting that while the content may be appropriate, it remains complex and potentially challenging for some users. 💡 Main outcomes and implications: - ⚠️ The presence of "hallucinations" in both models emphasizes the critical need for domain expertise when interpreting AI-generated recommendations in clinical settings. - 🧠 Clinicians must be aware of the strengths and limitations of LLMs to ensure safe and effective use in critical care, as reliance on these tools without proper understanding can lead to misinformation. - 🔍 The study calls for further research into enhancing the consistency and reliability of AI models in medical applications. 📚 Field significance: - 🌐 This research contributes to the ongoing discourse on the integration of AI in healthcare, particularly in critical care, where accurate and reliable information is paramount. - 🏥 Understanding the performance of LLMs like ChatGPT can guide future developments and training of AI systems to better serve clinical needs. - 📈 The findings underscore the importance of continuous evaluation and adaptation of AI tools in medical practice to align with evolving clinical standards. 🗄️: [#ChatGPT #CriticalCare #ArtificialIntelligence #Healthcare #ClinicalRecommendations #DataInsights #MedicalAI #LLM #Misinformation]