Enhancing Clinical Reasoning with Large Language Models via Knowledge Seeds

Keskeiset käsitteet

The author introduces a novel framework, In-Context Padding (ICP), to enhance Large Language Models (LLMs) with medical knowledge, significantly improving their clinical reasoning ability. By inferring critical clinical reasoning elements known as knowledge seeds, LLMs are guided in generating more accurate and interpretable results.

Tiivistelmä

The study explores the challenges of clinical reasoning in healthcare and proposes a novel framework, In-Context Padding (ICP), to enhance Large Language Models (LLMs) with medical knowledge. The ICP framework significantly improves the accuracy and interpretability of LLMs in clinical reasoning tasks by guiding them with inferred critical clinical reasoning elements called knowledge seeds. Experiments on two datasets demonstrate the effectiveness of ICP in bridging the gap between LLMs and medical expertise.
Key points:

Clinical reasoning is crucial for healthcare professionals to assess, diagnose, and treat patients.
Large Language Models (LLMs) have shown potential in medical applications but face challenges like hallucination during clinical reasoning.
The In-Context Padding (ICP) framework enhances LLMs by inferring knowledge seeds to guide their inference process.
Experiments on two datasets show that ICP significantly improves the accuracy and interpretability of LLMs in clinical reasoning tasks.

Tilastot

Accurate clinical reasoning requires extensive medical knowledge and rich clinical experience.
Recently, large language models such as ChatGPT and GPT-4 have demonstrated potential in clinical reasoning.
The proposed In-Context Padding (ICP) significantly improves the clinical reasoning ability of LLMs.
Experimental results on two datasets highlight a significant improvement in both accuracy and interpretability of LLMs.

Lainaukset

"Accurate clinical reasoning requires extensive medical knowledge and rich clinical experience."
"Large language models such as ChatGPT and GPT-4 have demonstrated potential in clinical reasoning."
"The proposed In-Context Padding (ICP) significantly improves the clinical reasoning ability of LLMs."

Tärkeimmät oivallukset

Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds

by Jiageng WU,X... klo arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06609.pdf

Syvällisempiä Kysymyksiä

How can the In-Context Padding framework be adapted for other specialized domains beyond healthcare?

The In-Context Padding (ICP) framework can be adapted for other specialized domains by customizing the knowledge seeds and training data to suit the specific domain requirements. Here are some steps to adapt ICP for other domains:

Identify Domain-Specific Knowledge Seeds: Instead of medical entities, relevant entities from the specific domain should be identified. For example, in legal settings, key legal terms or case precedents could serve as knowledge seeds.
Construct a Domain-Specific Knowledge Graph: Develop a knowledge graph that captures relationships between entities in the new domain. This graph will guide the selection of relevant knowledge seeds.
Mine Potential Knowledge Seeds: Use the constructed knowledge graph to mine potential knowledge seeds that are crucial for reasoning in that particular domain.
Guide Reasoning with Knowledge Seeds: Incorporate these identified knowledge seeds into prompts given to large language models to steer their inference process effectively.

By following these steps and tailoring them to different specialized fields such as law, finance, or engineering, the ICP framework can enhance LLMs' performance and reasoning abilities across various domains.

What are some potential drawbacks or limitations of relying heavily on large language models for critical decision-making processes?

While large language models (LLMs) offer significant advantages in various applications, there are several drawbacks and limitations when relying heavily on them for critical decision-making processes:

Lack of Explainability: LLMs often provide answers without clear explanations of how they arrived at those conclusions, making it challenging to understand their reasoning process.
Bias and Fairness Issues: LLMs may perpetuate biases present in their training data, leading to unfair decisions or recommendations based on historical prejudices.
Limited Context Understanding: Despite advancements, LLMs may struggle with understanding nuanced contexts or complex scenarios where human judgment is essential.
Data Dependency: The performance of LLMs is highly dependent on the quality and quantity of training data available; insufficient or biased data can impact their accuracy.
Ethical Concerns: Using AI systems like LLMs for critical decision-making raises ethical concerns regarding accountability, transparency, privacy, and consent.

Considering these limitations is crucial when integrating LLMs into decision-making processes where high stakes are involved.

How might advancements in AI technology impact the future role of human clinicians in healthcare settings?

Advancements in AI technology have profound implications for the future role of human clinicians in healthcare settings:

Enhanced Diagnostics: AI tools can assist clinicians by providing more accurate diagnoses through image analysis and pattern recognition algorithms.
Personalized Treatment Plans: AI-driven predictive analytics can help tailor treatment plans based on individual patient characteristics and medical histories.
Efficient Administrative Tasks: Automation through AI streamlines administrative tasks like scheduling appointments and managing electronic health records.
4 .Telemedicine Expansion: AI-powered telemedicine platforms enable remote consultations and monitoring services accessible from anywhere at any time
5 .Clinical Decision Support Systems:
AI-based clinical decision support systems offer real-time guidance on treatment options,
drug interactions,
and best practices based on current research evidence

Overall,
while AI technologies complement clinical practice by improving efficiency
and accuracy,
they cannot replace human clinicians' empathy,
ethical judgment,
and critical thinking skills essential aspects
of patient care that require a human touch