Erik Widman, Ph.D.

👉🏼 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]

👉🏼 Accuracy, readability, and understandability of large language models for prostate cancer information to the public 🤓 Jacob S Hershenhouse 👇🏻 https://lnkd.in/ek8wbVfg 🔍 Focus on data insights: - GPT-generated output was deemed correct by 71.7% to 94.3% of raters across 9 scenarios. - GPT-generated simplified layperson summaries were rated as accurate in 8 of 9 scenarios. - Mean readability of layperson summaries was significantly higher than original GPT outputs. 💡 Main outcomes and implications: - GPT shows promise for correct patient education for prostate cancer-related content. - Enhancing accuracy, completeness, clarity, and readability of GPT responses can improve its utility in medical chatbots. 📚 Field significance: - Improving the quality of information provided by large language models can enhance patient education and decision-making in healthcare. - Optimizing language model outputs for patient consumption can bridge the gap between complex medical information and public understanding. 🗄️: [#prostatecancer #GPT #patienteducation #healthcare #medicalchatbots]

What is agent ai? I really like this snippet from openai/swarm explaining agents. "An Agent simply encapsulates a set of instructions with a set of functions (plus some additional settings), and has the capability to hand off execution to another Agent." Part of that flow can be more rigid for more control. If you have an agent that can process refunds, you might want to have a human review it first (HITL). Or you might have a business workflow that is always the same and doesn't need an AI to make decisions inside it once it's triggered. This agency with control idea is what tools like @langchain's langgraph are solving for. But at it's core, an agent is just a set of instructions and a set of functions it can decide to use.

👉🏼 Large language models reduce public knowledge sharing on online Q&A platforms 🤓 R Maria Del Rio-Chanona 👇🏻 https://lnkd.in/eBwEnauA 🔍 Focus on data insights: - 📉 A 25% decrease in activity on Stack Overflow was observed within six months of ChatGPT's release, indicating a possible shift in user engagement. - 🌍 This reduction was significantly greater in regions with access to ChatGPT compared to Russian and Chinese counterparts where access is limited. - 🧑‍💻 The decline was consistent across posts related to widely used programming languages, emphasizing a broad impact across technical topics. 💡 Main outcomes and implications: - 🚫 The study suggests that LLMs like ChatGPT may be displacing not only low-quality or duplicate content but also original contributions from skilled users. - 🔍 There was no significant change in the quality of posts as measured by peer feedback, indicating that the reduction in quantity does not correlate with a drop in content quality. - 📊 The findings raise concerns about the sustainability of public knowledge-sharing platforms and the potential long-term effects on the availability of human-generated data for future model training. 📚 Field significance: - 📈 The research highlights the need to understand the dynamics between AI tools and human contribution to online knowledge bases. - 🤖 It calls into question the balance between leveraging LLMs for efficiency and preserving essential human-generated data. - 🛠️ Insights from the study could inform future discussions on designing platforms that encourage both AI use and active human participation. 🗄️: [#large_language_models #data_insights #public_knowledge #AI_impact #Stack_Overflow #ChatGPT #user_engagement #content_creation]

👉🏼 Can Artificial Intelligence Fool Residency Selection Committees? Analysis of Personal Statements by Real Applicants and Generative AI, a Randomized, Single-Blind Multicenter Study 🤓 Zachary C Lum 👇🏻 https://lnkd.in/e3wMDPm5 🔍 Focus on data insights: - 📊 Faculty identified real personal statements with 88% accuracy. - 🤖 ChatGPT's statements were only correctly recognized 44% of the time by reviewers. - 📉 No improvement in identification accuracy as more statements were reviewed

👉🏼 GENEVIC: GENetic data exploration and visualization via intelligent interactive console 🤓 Anindita Nath 👇🏻 https://lnkd.in/ejQG-BX5 🔍 Focus on data insights: - 📊 GENEVIC automates the analysis of genetic data, significantly reducing the time researchers spend on data interpretation. - 🔍 It integrates multiple data sources, allowing for comprehensive searches across PubMed, Google Scholar, and arXiv, enhancing literature review efficiency. - 🧬 The tool prioritizes genetic variants linked to complex diseases, aiding in targeted research and potential therapeutic developments. 💡 Main outcomes and implications: - 🚀 By streamlining the process of genetic data exploration, GENEVIC empowers researchers to make informed decisions quickly, potentially accelerating biomedical discoveries. - 🛠️ The user-friendly interface democratizes access to advanced genetic analysis tools, making them available to researchers without specialized training. - 🔒 With HIPAA-compliant infrastructure, GENEVIC ensures that sensitive genetic data is handled securely, fostering trust in its use within the biomedical community. 📚 Field significance: - 🌐 GENEVIC represents a significant advancement in bioinformatics, bridging the gap between data generation and actionable biomedical insights. - 🧩 The integration of generative AI into genetic research tools marks a transformative shift in how researchers interact with complex datasets. - 📈 As a prototype, GENEVIC sets a precedent for future innovations in genetic data analysis, paving the way for more sophisticated tools in the field. 🗄️: [#bioinformatics #geneticdata #AI #biomedicalresearch #dataanalysis #geneticvariants #healthcare #machinelearning]

👉🏼 A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions 🤓 Scott P McGrath 👇🏻 https://lnkd.in/eBHetz2N 🔍 Focus on data insights: - Improved accuracy in GPT-4 over GPT-3.5 (P < .0001) - Notable errors in accuracy remained - Relevance of responses varied in GPT-4, generally favorable 💡 Main outcomes and implications: - GPT-4 shows potential in genetic education with notable progress - Challenges include outdated information and the need for ongoing refinement - Emphasizes balancing technological innovation with ethical responsibility in healthcare information delivery 📚 Field significance: - Genetic information delivery - Technology in medical settings - Ethical implications in healthcare 🗄️: [#genetics #ChatGPT #datainsights #medicaltechnology]

👉🏼 On-device query intent prediction with lightweight LLMs to support ubiquitous conversations 🤓 Mateusz Dubiel 👇🏻 https://lnkd.in/ekpGf3ct 🔍 Focus on data insights: - Lightweight pre-trained LLMs fine-tuned for on-device query intent prediction - Transfer learning techniques applied to enhance flexibility and scalability of conversational agents - Privacy-preserving scenarios enabled by on-device deployment of LLMs 💡 Main outcomes and implications: - Improved performance of RoBERTa and XLNet in predicting user query intent - Comparable performance of fine-tuned LLMs with ChatGPT - Highlighting the balance between LLM performance, memory footprint, and privacy considerations 📚 Field significance: - Advancing the field of conversational agents towards more flexible and scalable models - Addressing privacy concerns by enabling on-device deployment of LLMs - Providing insights for researchers and practitioners on leveraging LLMs for personalized and privacy-preserving conversational experiences 🗄️: #ConversationalAgents #LLMs #OnDeviceDeployment #PrivacyPreservation #QueryIntentPrediction

👉🏼 ChatGPT-4's Efficacy in Providing Insights on Managing Polycystic Ovary Syndrome (PCOS) and Infertility 🤓 Belgin Devranoglu https://lnkd.in/eUHzfWgf 🔍 Focus on data insights: - ChatGPT-4 achieved a flawless accuracy rate of 100% for true/false queries. - Significant improvement in accuracy and completeness scores for open-ended queries. - Consistent performance in handling multiple-choice questions. 💡 Main outcomes and implications: - AI, particularly ChatGPT-4, shows potential in enhancing decision-making support for healthcare professionals managing PCOS-related infertility. - Improved accuracy and completeness in responding to a wide range of PCOS scenarios. 📚 Field significance: - Advancements in AI technology like ChatGPT-4 can revolutionize the way healthcare professionals access information and make decisions in managing complex medical conditions. - Data-driven insights from AI models can provide valuable support in addressing specific challenges in the field of reproductive health. 🗄️: #AI #PCOS #infertility #healthcareprofessionals

👉🏼 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]

👉🏼 Opportunities and risks involved in using ChatGPT to create first grade science lesson plans 🤓 Wardell Powell 👇🏻 https://lnkd.in/egj2c-xR 🔍 Focus on data insights: - Generative AI efficiency in lesson planning - Alignment with curriculum frameworks - Identification of questionable components 💡 Main outcomes and implications: - Efficient generation and refinement of lesson plans - Importance of detailed review and validation - Implications for training pre-service teachers 📚 Field significance: - Integration of AI in education - Enhancing lesson planning processes - Training implications for educators 🗄️: [#GenerativeAI #LessonPlanning #CurriculumAlignment #TeacherTraining]

👉🏼 Scaffolding Learning: From Specific to Generic with Large Language Models 🤓 David S Yin 👇🏻 https://lnkd.in/etuG7fu4 🔍 Focus on data insights: - 📊 Large language models

👉🏼 ChatGPT-based Biological and Psychological Data Imputation 🤓 Anam Nazir https://lnkd.in/ezAXTbzp 🔍 Focus on data insights: - ChatGPT utilized for biological and psychological data imputation - Evaluation metrics include Pearson correlation coefficient, relative accuracy, and mean absolute error 💡 Main outcomes and implications: - ChatGPT shows superior efficacy compared to traditional imputation methods - Customized data-to-text prompting enhances accuracy in imputations 📚 Field significance: - Improved accuracy in imputing biological and psychological data - Potential for enhancing research outcomes in large cohort studies 🗄️: #datainsights #imputation #biologicaldata #psychologicaldata

Why Most Cross-Validation Visualizations Are Wrong (And How to Fix Them) https://ift.tt/3Im8iwz MODEL VALIDATION & OPTIMIZATION Stop using moving boxes to explain cross-validation! You know those cross-validation diagrams in every data science tutorial? The ones showing boxes in different colors moving around to explain how we split data for training and testing? Like this one: Have you seen that? Image by author. I’ve seen them too — one too many times. These diagrams are common — they’ve become the go-to way to explain cross-validation. But here’s something interesting I noticed while looking at them as both a designer and data scientist. When we look at a yellow box moving to different spots, our brain automatically sees it as one box moving around. It’s just how our brains work — when we see something similar move to a new spot, we think it’s the same thing. (This is actually why cartoons and animations work!) You might think the animated version is better, but now you can’t help following the blue box and starting to forget that this should represent how cross-validation works. Source: Wikipedia But here’s the thing: In these diagrams, each box in a new position is supposed to show a different chunk of data. So while our brain naturally wants to track the boxes, we have to tell our brain, “No, no, that’s not one box moving — they’re different boxes!” It’s like we’re fighting against how our brain naturally works, just to understand what the diagram means. Looking at this as someone who works with both design and data, I started thinking: maybe there’s a better way? What if we could show cross-validation in a way that actually works with how our brain processes information? All visuals: Author-created using Canva Pro. Optimized for mobile; may appear oversized on desktop. What’s Cross-Validation Really About? Cross-validation is about making sure machine learning models work well in the real world. Instead of testing a model once, we test it multiple times using different parts of our data. This helps us understand how the model will perform with new, unseen data. Here’s what happens: We take our data Divide it into groups Use some groups for training, others for testing Repeat this process with different groupings The goal is to get a reliable understanding of our model’s performance. That’s the core idea — simple and practical. (Note: We’ll discuss different validation techniques and their applications in another article. For now, let’s focus on understanding the basic concept and why current visualization methods need improvement.) What’s Wrong with Current Cross-validation Diagrams? Open up any machine learning tutorial, and you’ll probably see these types of diagrams: Long boxes split into different sections Arrows showing parts moving around Different colors showing training and testing data Multiple versions of the same diagram side by side Currently, this is similar to the first image you’ll see if...