Enterprise AI Analysis
Comparative Performance of LLMs and Pediatric Residents on In-Training Examinations
This analysis evaluates the performance of recent and prior large language models (LLMs) against pediatric residents on in-training examination questions, highlighting their capabilities in a medical context.
Authors: Mi Jin Kim, Jun Sung Park, Sung Han Kang
Executive Impact: Key Performance Indicators
Gain critical insights into the real-world implications and measurable performance of advanced AI in specialized medical fields.
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Recent LLM Exam PC (vs R4 70.1%)
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LLM Output Repeatability
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Image-Based Qs PC (Recent LLMs)
Deep Analysis & Enterprise Applications
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77.7–78.9%
PC for recent LLMs vs. 70.1% for R4 (P < 0.008)
Recent vision-enabled multimodal large language models (LLMs) significantly outperformed fourth-year residents (R4) on the pediatric in-training examination questions, demonstrating superior accuracy across the board.
>0.98
Intraclass Correlation Coefficient (ICC)
The study observed excellent repeatability of outputs from most LLMs, with an intraclass correlation coefficient exceeding 0.98, indicating consistent performance across multiple trials.
Enterprise Process Flow
12 ITEs (2016-2023) identified
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Excluded for prompt optimization/lost answer sheets (10 ITEs remaining)
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48 duplicated items removed (498 unique questions)
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Korean/English terminology; 22% included medical images
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Original examination sheets used without modification
The study meticulously prepared a dataset of 498 unique pediatric in-training examination questions, ensuring the integrity and representativeness of the original examination format.
| Category | Recent LLMs (PC) | R4 (PC) | Key Finding |
|---|---|---|---|
| Overall Questions | 77.7-78.9% | 70.1% | Recent LLMs significantly outperformed R4. |
| Text-Only Questions | 80.1-81.0% | 69.6% | Recent LLMs showed superior PC compared to R4. |
| Image-Included Questions | <73.9% | Comparable to R4 | None of the LLMs surpassed R4 performance; both prior and recent models exhibited inferior PC on image-included items than text-only questions. |
While recent LLMs excelled on text-only questions, their performance on image-included questions remained comparable to or even inferior to senior residents, highlighting a crucial area for improvement in visual reasoning.
Educational Utility and Future Potential of LLMs
Scenario: The high accuracy of LLMs on ITEs suggests significant educational utility. As adjunctive study tools, LLMs can provide trainees with rapid feedback, exposure to diverse clinical scenarios, and opportunities to practice critical appraisal of AI-generated answers.
Challenge: However, these results are not proof of readiness for autonomous pediatric Clinical Decision Support Systems (CDSS). Further validation on real-world cases and external validation are required.
Outcome: LLMs offer a strong potential to complement traditional pediatric education, fostering improved learning outcomes and critical thinking skills in trainees, but their role remains assistive for now.
73.9%
Recent LLMs PC on image-included questions (vs. 80.4% on text-only)
Despite advancements, recent multimodal LLMs achieved significantly lower proportion correct on image-included questions (73.9%) compared to text-only questions (80.4%), highlighting a persistent limitation in visual reasoning for pediatric contexts.
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