Tag: LLMs
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What 2026 Might Look Like, According to AI Forecasts: The Rise of Model-Driven Predictions
Predictions in 2026: Why AI Keeps Being Asked to Look Ahead For years, people have asked machines to predict the future. In 2024, a Wharton study highlighted a striking shift: pooling predictions from multiple large language models (LLMs) yielded results on par with expert human forecasters. That finding wasn’t a one-off curiosity; it underscored a…
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What 2026 Might Look Like: AI Chatbots and the New Era of Forecasting
Why AI chatbots are predicting the future differently As artificial intelligence evolves, so does the way we think about predicting the near future. Recent work suggests that pooling predictions from multiple large language models (LLMs) can yield results that rival, and in some cases match, expert human forecasters. This isn’t about a single AI declaring…
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What 2026 Might Look Like: AI Chatbots Agree on One Thing
What the Bots Are Saying About 2026 As we edge toward 2026, a growing thread in tech journalism is not just what humans think the future will hold, but what AI chatbots predict. A striking pattern has emerged: despite different training data and architectures, many large language models (LLMs) converge on a single, notable trend…
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Enhancing Cardiovascular Nutrition Guidance: A Cross-Sectional Evaluation of LLMs with Retrieval-Augmented Generation
Introduction: The Promise and Peril of AI in Cardiovascular Nutrition As digital health tools proliferate, large language models (LLMs) and generative AI offer the potential to scale evidence-based nutrition education for cardiovascular disease (CVD) prevention. Grounded in the American Heart Association’s (AHA) guideline framework, these technologies aim to improve health literacy while ensuring information reliability.…
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Large Language Models in Lung Cancer: A Comprehensive Systematic Review
Introduction: The Rise of LLMs in Lung Cancer Care Large language models (LLMs) are increasingly explored as tools to assist in the full cycle of lung cancer (LC) management, from prevention and screening to diagnosis, treatment planning, and supportive care. This systematic review synthesizes recent evidence on how LLMs are being applied to LC, what…
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A Human-LLM Collaborative Annotation Approach for Screening Precision Oncology Randomized Controlled Trials
Introduction Systematic reviews in precision oncology rely on screening thousands of articles to identify randomized controlled trials that evaluate targeted therapies, biomarkers, and patient outcomes. This manual annotation process is labor-intensive, time-consuming, and susceptible to variability across reviewers. Large language models (LLMs) offer rapid classification and data extraction, but their reliability can be uneven without…
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A human-LLM collaborative annotation approach for screening articles on precision oncology randomized controlled trials
Why a human-LLM collaborative approach matters Systematic reviews in precision oncology require screening thousands of articles to identify randomized controlled trials (RCTs) that illuminate biomarker-driven therapies and targeted interventions. Manual screening, while thorough, is time-consuming and resource-intensive. Large language models (LLMs) can accelerate triage by quickly categorizing relevance and extracting key trial details, but their…
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Understanding Doomprompting: The AI Pitfall You Need to Avoid
What is Doomprompting? Doomprompting is a recent phenomenon observed among AI users, particularly in the context of large language models (LLMs) like ChatGPT. This behavior mirrors doomscrolling, where users compulsively consume negative news, leading to a pervasive sense of hopelessness. In contrast, doomprompting involves the endless tweaking of prompts and results from AI, resulting in…