Language Models And A Second Opinion Use Case: The Pocket Professional
This research tests the role of Large Language Models (LLMs) as formal second
opinion tools in professional decision-making, particularly focusing on complex
medical cases where even experienced physicians seek peer consultation. The
work analyzed 183 challenging medical cases from Medscape over a 20-month
period, testing multiple LLMs' performance against crowd-sourced physician
responses. A key finding was the high overall score possible in the latest
foundational models (>80% accuracy compared to consensus opinion), which
exceeds most human metrics reported on the same clinical cases (450 pages of
patient profiles, test results). The study rates the LLMs' performance
disparity between straightforward cases (>81% accuracy) and complex scenarios
(43% accuracy), particularly in these cases generating substantial debate among
human physicians. The research demonstrates that LLMs may be valuable as
generators of comprehensive differential diagnoses rather than as primary
diagnostic tools, potentially helping to counter cognitive biases in clinical
decision-making, reduce cognitive loads, and thus remove some sources of
medical error. The inclusion of a second comparative legal dataset (Supreme
Court cases, N=21) provides added empirical context to the AI use to foster
second opinions, though these legal challenges proved considerably easier for
LLMs to analyze. In addition to the original contributions of empirical
evidence for LLM accuracy, the research aggregated a novel benchmark for others
to score highly contested question and answer reliability between both LLMs and
disagreeing human practitioners. These results suggest that the optimal
deployment of LLMs in professional settings may differ substantially from
current approaches that emphasize automation of routine tasks.
672069e193f3d8192f1d7f7a
David Noever
claimed_verified
2025-01-28T10:26:46.170Z
63136a82e29fb2e86d5e5bdd
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David Noever
user
dnoever
Language Models And A Second Opinion Use Case: The Pocket Professional
This research tests the role of Large Language Models (LLMs) as formal second
opinion tools in professional decision-making, particularly focusing on complex
medical cases where even experienced physicians seek peer consultation. The
work analyzed 183 challenging medical cases from Medscape over a 20-month
period, testing multiple LLMs' performance against crowd-sourced physician
responses. A key finding was the high overall score possible in the latest
foundational models (>80% accuracy compared to consensus opinion), which
exceeds most human metrics reported on the same clinical cases (450 pages of
patient profiles, test results). The study rates the LLMs' performance
disparity between straightforward cases (>81% accuracy) and complex scenarios
(43% accuracy), particularly in these cases generating substantial debate among
human physicians. The research demonstrates that LLMs may be valuable as
generators of comprehensive differential diagnoses rather than as primary
diagnostic tools, potentially helping to counter cognitive biases in clinical
decision-making, reduce cognitive loads, and thus remove some sources of
medical error. The inclusion of a second comparative legal dataset (Supreme
Court cases, N=21) provides added empirical context to the AI use to foster
second opinions, though these legal challenges proved considerably easier for
LLMs to analyze. In addition to the original contributions of empirical
evidence for LLM accuracy, the research aggregated a novel benchmark for others
to score highly contested question and answer reliability between both LLMs and
disagreeing human practitioners. These results suggest that the optimal
deployment of LLMs in professional settings may differ substantially from
current approaches that emphasize automation of routine tasks.
LLMs demonstrate significant potential as second opinion tools in professional decision-making, particularly in medical diagnostics, by generating comprehensive differential diagnoses and augmenting clinical decision-making.
Large Language Models LLMs formal second opinion tools professional decision-making complex medical cases Medscape foundational models crowd-sourced physician responses clinical cases differential diagnoses cognitive biases cognitive loads medical error Supreme Court cases