LLM vs SDPM for Predictive Task Performance

This paper evaluates whether LLMs can outperform traditional ML models in clinical prediction tasks by developing the "ClinicalBench" benchmark, which tests 22 LLMs and 11 traditional ML models across three tasks using real-world datasets. 1️⃣ Traditional ML models consistently outperform both general-purpose and medical LLMs in Length-of-Stay, Mortality, and Readmission predictions. 2️⃣ Fine-tuning improves LLM performance in some tasks (e.g., Length-of-Stay, Mortality) but not enough to surpass traditional ML models. 3️⃣ Prompt engineering strategies (e.g., In-Context Learning) have limited impact, failing to close the performance gap between LLMs and ML models. 4️⃣ Medical-specific LLMs do not consistently outperform general-purpose LLMs, with some domain-specific adaptations even degrading performance. 5️⃣ LLM predictions often lack balance, exhibiting high false-positive rates that undermine precision and F1 scores. 6️⃣ Traditional ML models exhibit strong, stable performance even with reduced training data, making them more suitable for low-resource scenarios. 7️⃣ Increasing LLM size or adjusting parameters (e.g., decoding temperature) yields inconsistent improvements, often failing to align with clinical needs. 8️⃣ Clinical reasoning and decision-making tasks reveal fundamental deficiencies in LLMs, highlighting the limitations of their knowledge transfer from text-based benchmarks. 9️⃣ The study underscores the need for clinically relevant training data to address gaps in LLMs’ real-world healthcare performance. 🔟 The findings advocate for caution in adopting LLMs for clinical applications, urging focus on bridging the gap between their development and practical utility. ✍🏻 Canyu Chen, Jian Yu, Shan Chen, Liu Che, Zhongwei Wan, Danielle Bitterman, Fei Wang, Kai Shu. ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction? arXiv. 2024. DOI: 10.48550/arXiv.2411.06469

🤔 Think the latest AI models always outperform older techniques? Think again. A recent study highlights that in certain areas, especially in healthcare, traditional machine learning methods still have the upper hand over the newest Large Language Models (LLMs). What's the Study About? Researchers focused on clinical prediction tasks - such as: - Length of Stay Prediction: Estimating how long a patient will remain hospitalized. - Mortality Prediction: Assessing the risk of patient death. - Readmission Prediction: Predicting the likelihood of a patient needing to return to the hospital after discharge. They introduced a new benchmark called ClinicalBench to compare different AI models on these tasks. Key highlights: - Traditional ML Models Outperform LLMs: Classic machine learning algorithms outshined both general-purpose and medical-specific LLMs in predicting patient outcomes. - Medical Specific LLMs Aren't Significantly Better: LLMs tailored for medical use didn't show notable improvements over general LLMs of similar sizes. - Advanced Prompting Techniques Fell Short: Methods like giving LLMs step-by-step reasoning prompts (Zero-shot Chain-of-Thought), having them reflect on their answers (Self-Reflection), role-playing scenarios, or providing examples in prompts (In-Context Learning) offered limited gains but didn't surpass traditional ML. - Fine Tuning Helps, But Not Enough: Adjusting LLMs with specific medical data (fine-tuning) did improve their performance in some tasks, like Length-of-Stay and Mortality Prediction, but they still generally lagged behind traditional models. 🤔 Why Aren't LLMs Excelling Here? One reason that is sometimes discussed is that LLMs lack access to detailed, real-world patient data during their training. Without this relevant information, they struggle to make accurate clinical predictions. This serves as a valuable reminder: - Newer Isn't Always Better: While LLMs are powerful and versatile, they're not a one-size-fits-all solution. - Cost-Effectiveness Matters: Traditional ML models are not only performing better in these cases but are also more resource-efficient. - Choose the Right Tool: It's crucial to select the appropriate technology based on the specific problem, rather than defaulting to the latest trend. Innovation drives progress, but it's important to balance excitement for new technologies with practical effectiveness. In specialized fields like healthcare, sometimes traditional methods remain the best choice. What other areas do you think might not gain enough added value from LLM-based solutions to justify the investment? #innovation #technology #future #management #startups

🥲 A great example of why you shouldn’t use LLMs just for the sake of it—there are still plenty of fields where traditional ML methods outperform LLMs while being far more cost-effective. This paper shows results on clinical prediction tasks like Length-of-Stay Prediction, Mortality Prediction, and Readmission Prediction and introduces a new benchmark called ClinicalBench to prove the above. Key Findings: ⛳ Traditional ML models outperform both general-purpose and medical LLMs in clinical prediction tasks. ⛳ Medical-specific LLMs show no significant advantage over general-purpose LLMs of similar size. ⛳ Techniques like Zero-shot Chain-of-Thought, Self-Reflection, Role-Playing, and In-Context Learning offer limited improvements but fail to surpass traditional ⛳ Fine-tuned LLMs show some improvements in tasks like Length-of-Stay and Mortality Prediction, but still fall short of traditional ML models in most cases. One hypothesis for LLMs’ underperformance is the lack of realistic and relevant patient data during their pre-training and fine-tuning. Link: https://lnkd.in/euQREN-x