Tiga Healthcare Technologies 的动态

I find this article of Stanford Institute for Human-Centered Artificial Intelligence (HAI) particularly compelling as it discusses the increasing presence of LLMs such as ChatGPT in the healthcare sector. It emphasizes the importance of evaluating their reliability, despite their potential to assist in diagnosis. However, lingering concerns about their accuracy remain prevalent. ?? Verifying References: A Key Challenge ? Recent study highlights LLMs' struggle to cite medical sources accurately. ? 30% of statements from advanced models like GPT-4 remain unsupported. Even with retrieval augmented generation (RAG) models, errors persist. ?? Evaluating Performance: ? LLMs perform best with inquiries based on professional medical texts. ? Lay inquiries, particularly from platforms like Reddit, pose greater challenges. ?? Importance of Source Verification: ? Health knowledge democratization hinges on LLMs' ability to provide reliable information. ? Currently, LLMs fall short, raising concerns about their distributive effects on health knowledge. ?? Looking Ahead: ? Research should focus on domain-specific adaptations, like RAG for medical use. ? Regular evaluation of source verification is crucial for ensuring credibility. ???? Regulatory Considerations: ? As LLMs gain prominence, regulators and healthcare providers must scrutinize their integration and reliability. Link to the article: https://lnkd.in/ebMcF64p Thanks to the authors: Kevin Wu Eric Wu Daniel E. Ho James Zou Other leaders working and advising in generative AI: Chaitanya Adabala Viswa Delphine Nain Zurkiya Joachim Bleys Bhavik Shah Eoin Leydon Mahmoud Abu Eid ASLI AKSU Eric Bruckner Lucia Darino Supreet Deshpande Alex Devereson Aliza Dzik Anas El Turabi Lionel Jin Matej Macak Abhi Raj Rajendran Boyd Spencer Hann-Shuin Yew Stephen Chase Amy Matsuo Emily Frolick Bryan McGowan Brian Consolvo Kanika Saraiya Havelia Christopher Montgomery Meg Smiley Wheaton The journey toward harnessing LLMs' potential in healthcare requires rigorous evaluation and continuous improvement.

Understanding the difference between pre-tuning and post-tuning large language models (LLMs) is crucial for medical facilities looking to implement AI solutions. Here’s a quick guide to help clarify these concepts: Pre-Tuning: Pre-tuning involves training an LLM on a broad dataset before applying it to specific tasks. This process establishes a foundational understanding of language and general knowledge. For medical facilities, pre-tuned models are beneficial as they already grasp basic medical terminology and concepts, making them versatile and ready for further customization. Post-Tuning: Post-tuning, or fine-tuning, is the process of further training an already pre-tuned LLM using specific datasets related to a particular field or application. For medical facilities, this means taking a pre-tuned model and refining it with your own data, such as anonymized patient records and clinical notes. Post-tuning ensures the model is highly specialized and accurate for your specific needs. Key Differences and Benefits: Scope of Knowledge: Pre-tuned models have a broad, general understanding, while post-tuned models are specialized with in-depth knowledge of specific medical data. Flexibility vs. Precision: Pre-tuned models offer flexibility and can be adapted to various tasks. Post-tuned models provide precision, tailored to the specific needs of your facility. Implementation Speed: Pre-tuned models are quicker to deploy but may lack the detailed accuracy needed for specialized tasks. Post-tuning takes additional time but results in a model that precisely fits your requirements. By understanding and utilizing both pre-tuning and post-tuning, medical facilities can implement AI solutions that enhance patient care, improve diagnostics, and streamline operations. #HealthcareInnovation #AIinMedicine #PatientCare #MedTech

As a patient who has relied on LLMs for over a year to support medical decisions for myself and a loved one, I found this article by Scott Gottlieb and Shani Benezra really validating. The authors compared the performance of five leading AI chatbots (ChatGPT, Anthropic Claude, Google Gemini, Grok, and HuggingChat) on the USMLE Step 3 exam. The results show that consumer-facing frontier models have a surprising aptitude for clinical reasoning. #PatientsUseAI

Many consumers and medical providers are turning to chatbots, powered by large language models to answer medical questions and inform treatment choices. Five major large language models was subjected to parts of the U.S. Medical Licensing Examination Step 3 examination, widely regarded as the most challenging. Here’s how ChatGPT, Claude, Google Gemini, Grok and Llama performed. ChatGPT-4o (OpenAI) — 49/50 questions correct (98%) Claude 3.5 (Anthropic) — 45/50 (90%) Gemini Advanced (Google) — 43/50 (86%) Grok (xAI) — 42/50 (84%) HuggingChat (Llama) — 33/50 (66%) #AI #LLM #healthcare #doctors Scott Gottlieb https://lnkd.in/dqAvmN-9

This article from MedCity News discusses how large language models (LLMs) like ChatGPT can enhance the patient experience in healthcare. The piece highlights the potential of using LLMs in tasks such as answering patient queries, providing personalized health information, and improving communication between patients and healthcare providers. Additionally, the article explores the challenges and ethical considerations associated with implementing LLMs in healthcare settings. https://lnkd.in/ea-EA3Na #healthcare #healthcareai #healthcareit #ai #genai #llm #generativeai #ChatGPT

AI in Healthcare: Transforming Patient Care ??? The integration of AI—especially Large Language Models (LLMs) and Generative AI—is reshaping healthcare. These cutting-edge technologies support clinicians by improving diagnostics, suggesting treatment options, and providing real-time insights from vast medical databases. ?? Key models like PubMedBERT, BioBERT, and Med-PaLM are driving innovation in clinical decision-making. However, challenges such as data privacy, biases, and ethical concerns remain. It’s crucial that AI complements medical expertise while ensuring transparency, fairness, and patient trust. ?? As AI evolves, it holds the promise of making healthcare more personalized, efficient, and accessible. The future is bright if handled responsibly! ?? https://lnkd.in/g92Dbp36 #AIinHealthcare #GenerativeAI #MedTech #HealthcareInnovation #FutureOfMedicine

Is it time to reevaluate the reliability of LLMs like ChatGPT in medicine? The increasing reliance on LLMs, including ChatGPT, in the medical field is prompting some timely critical examination of their reliability. A recent study by Stanford HAI reveals a troubling gap: even cutting-edge models often fail to substantiate their answers, casting doubt on their suitability for medical decision-making. As the role of AI in medicine continues to evolve, it's imperative that we prioritise the development and use of reliable, evidence-based tools that both support healthcare professionals and deliver optimal outcomes for patients. Metadvice is at the forefront of leveraging AI to manage long-term chronic conditions and therefore tackling the global challenges of healthcare systems. #AIHealthcare #DigitalHealth #PatientCare #Innovation

Reports tell us that doctors are increasingly using #ChatGPT in their day-to-day work, and a growing number of patients are using LLMs (large language models) to self-diagnose. This begs the question: Is ChatGPT gradually replacing the role of the Doctor? Moreover, is it safe? According to a recent study from the Stanford Institute for Human-Centered Artificial Intelligence (HAI), “Very little evidence exists about the ability of LLMs to substantiate claims," adding that most LLMs struggle to produce relevant sources, and ~30% of individual statements made on models like ChatGPT are unsupported. Yet there ARE opportunities for AI to transform the healthcare landscape now. Our AI-driven platform provides evidence-based recommendations, using the latest relevant guidelines, so clinicians can deliver informed, effective treatment. We look forward to seeing developments in this space, to support patients, clinicians, and health systems alike - with safety at the forefront. Read more about the study by Kevin Wu, Eric Wu, Daniel Ho, and James Zou: https://lnkd.in/gfcDDBut #HealthcareAI #LLMs #FutureofHealthcare