Pangaea Data’s Post

We are pleased to announce that our latest publication has been accepted by JAMIA. It is exciting to witness the potential and future works of large language models (LLMs) in the healthcare domain. Since the advent of Transformers, including the BERT and GPT families, there has been significant research into the application of language models in industrial sectors such as healthcare. Given the vast and ever-increasing volume of healthcare data, it is crucial to ensure that LLMs adhere to clinical guidelines and assist in finding patients for better outcomes through efficient training and human feedback. More work to come from Pangaea.

Today the most important article in chatgpt and "Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications" - Identified the applications, benefits, and challenges of Generative Artificial Intelligence (GAI) in healthcare. - Provided an overview of GAI's potential to revolutionize healthcare by enhancing diagnosis, treatment, and care through new data and insights. - Explored various GAI models used in healthcare, with ChatGPT being the most frequently employed. - Identified 24 different applications of GAI in healthcare, including offering insights on health conditions and disease diagnosis. - Highlighted 606 benefits and challenges of GAI, emphasizing rapid access to information, improved prediction accuracy, and challenges like generating inaccurate content. - Emphasized the imperative to address limitations of GAI in healthcare for its successful integration. #healthcare #artificialintelligence #GAI #benefits #challenges

👉🏼 ChatGPT Responses to Frequently Asked Questions on Ménière's Disease: A Comparison to Clinical Practice Guideline Answers 🤓 Rebecca A Ho 👇🏻 https://lnkd.in/e7Nvpwzw 🔍 Focus on data insights: - ChatGPT-4.0 had significantly better resource reporting compared to ChatGPT-3.5. - Both models showed room for improvement in comprehensiveness, readability, and accuracy. 💡 Main outcomes and implications: - ChatGPT-4.0 demonstrated higher quality resources in responses. - Readability scores for both models fell below the recommended level. - Improvement needed in providing more comprehensive and less misleading information to patients. 📚 Field significance: - Evaluating AI models like ChatGPT against clinical guidelines can help identify areas for enhancement in patient education. - Enhancing AI models' readability and accuracy is crucial for effective patient communication in healthcare settings. 🗄️: [#Ménière'sDisease #AIModelEvaluation #PatientEducation #HealthcareCommunication]

🎙️What is AI? More Importantly, Should I Be Worried? “Siri, make an appointment for my low back pain.” “Alexa, who is the best doctor to handle my cirrhosis?” As if the regulatory changes weren’t enough, we must also compete with technology. The presentation will cover the current scope of the various “phases” of the technology and how they apply to healthcare for the moment…. Finally, the presentation will explore and encourage the review and expansion of how artificial intelligence may shift our world of healthcare Cam will also lead a Breakout: Surviving Chaos: Private Practices Facing New Realities Don’t miss Cameron Cox, III, MHA, FACMPE at the #gmgmaannualconference24 Register at https://lnkd.in/ei5Qy-zf MGMA #speakerspotlight #heathcaremanagers #medicalpracticemanagers

What happens when AI meets clinical decision-making? Dr. Wachter demonstrates this by consulting ChatGPT on a patient case at the TDC Group 2024 Executive Advisory Board Meeting. This is the future of healthcare!

Fewer overworked doctors and fewer medical errors. A highly promising paper by Google DeepMind is currently making the rounds: Medical errors are the third-leading cause of death in the US, with 250,000 to 440,000 deaths per year (depending on estimate). AI could be the solution to this: Google has tweaked Gemini to create an astonishing medical AI system. #MedGemini performs exceptionally well on benchmarks and can address a wide array of medical issues. From a technological perspective, it is fascinating how the model was developed. When confronted with a question, MedGemini is able to enhance its accuracy by self-evaluating its confidence and using web searches to gather more information. This process involves generating reasoning paths, filtering them based on uncertainty, creating search queries to clarify ambiguities, and incorporating the results into the final responses. A general system like this in a specialized domain like medicine shows how far we have come over the last ten years. Read the full paper here: https://lnkd.in/dAaEtMua #MedGemini #AIinHealthcare #MedicalInnovation #HealthTech #ArtificialIntelligence #MedTech

👉🏼 Accuracy of Online Artificial Intelligence Models in Primary Care Settings 🤓 Joseph Kassab 👇🏻 https://lnkd.in/e2gxadaq 🔍 Focus on data insights: - ChatGPT-4 and Google Bard assessed for accuracy in providing recommendations in preventive medicine and primary care. - Inter-reviewer agreement on grading was substantial, with notable differences in accuracy rates between the two AI models. - Responses to CDC and immunization-related questions showed significant inaccuracies in both ChatGPT-4 and Google Bard. 💡 Main outcomes and implications: - ChatGPT-4 and Google Bard show potential in offering accurate information in preventive care. - Highlighted the critical need for regular updates in rapidly evolving medical areas. - Emphasized the importance of considering AI tools as complementary resources when seeking medical information. 📚 Field significance: - Assessing the accuracy of AI models in primary care settings is crucial for enhancing patient care and decision-making processes. - Understanding the limitations and strengths of AI tools can aid healthcare professionals in utilizing them effectively. - Continuous evaluation and improvement of AI models are essential to ensure reliable and up-to-date information in healthcare. 🗄️: #ArtificialIntelligence #PrimaryCare #DataInsights #HealthcareTechnology

👉🏼 Patient-Representing Population's Perceptions of GPT-Generated Versus Standard Emergency Department Discharge Instructions: Randomized Blind Survey Assessment 🤓 Thomas Huang 👇🏻 https://lnkd.in/ecNscY2E 🔍 Focus on data insights: - 📊 Survey respondents rated GPT-generated discharge instructions significantly more favorably for interpretability of return precautions (P=.01). - 📝 GPT-generated instructions were found to be more understandable across various metrics, including procedures and post-ED medications. - 👍 Satisfaction ratings for GPT-generated instructions were higher in key areas such as treatment and follow-up compared to standard instructions. 💡 Main outcomes and implications: - 🌟 The study highlights the potential of LLMs like ChatGPT to enhance the quality of discharge instructions in emergency departments. - 🔄 By improving readability and personalization, LLMs can reduce the documentation burden on healthcare providers. - 🏥 Enhanced communication methods could lead to better patient understanding and adherence to discharge instructions. 📚 Field significance: - 🚀 This research underscores the transformative role of AI in healthcare documentation and patient communication. - 💬 It opens avenues for further exploration into AI applications in clinical settings, particularly in enhancing patient education. - 🔍 The findings suggest a shift towards integrating AI tools in routine medical practices to improve patient outcomes. 🗄️: [#dischargeinstructions #AIinHealthcare #patientcommunication #LLMs #ChatGPT #emergencydepartment #healthcaredocumentation #patienteducation]

#AIandhealthcare #patientQAassistant #burnoutreduction According an original investigation from JAMA, AI ChatGPT performed quite well when rated for empathy and quality of responses addressing patients' questions posted to a public social media forum. Messaging volume can lead to increased burnout for clinicians with 62% of physicians reporting at least 1 burnout symptoms. Busy medical offices could also leverage this technology to pre-draft messages and responses for patients and free up staff and clinicians for more productive, complex and strategic tasks. There are limitations to this initial study such as being tested in a social media forum. Results are promising and should prompt further investigations and perhaps in actual clinical settings. For more posts on generative AI & LLMs please go to: https://cahir.ai/Blog/ Ayers, J., Poliak, A., & Dredze, M. (2023, April 28). Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine. https://lnkd.in/ggw69UpF

Big news for the world of AI and healthcare: My colleagues working on health LLMs at Google have developed a chatbot called Articulate Medical Intelligence Explorer (AMIE) that can take medical histories and diagnose patients with impressive accuracy. Here's what AMIE can do: - Match or surpass human doctors in diagnosing respiratory and cardiovascular conditions. - Score higher on empathy and politeness during consultations. - Help democratize healthcare by providing initial consultations and support, especially in underserved areas. But before you ditch your doctor: - AMIE is still under development. - Human connection and rapport are essential in healthcare, and AI can't fully replace that. #AI #healthcare #chatbot #diagnosis #ethics #futureofmedicine P.S. Check out the paper for more details: https://lnkd.in/gEGHjR-k