title: Language Models And A Second Opinion Use Case: The Pocket Professional
summary: 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.
paper_authors._id: 672069e193f3d8192f1d7f7a
paper_authors.name: David Noever
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-28T10:26:46.170Z
paper_authors._id: 63136a82e29fb2e86d5e5bdd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
paper_authors.fullname: David Noever
paper_authors.type: user
paper_authors.user: dnoever
paper_title: Language Models And A Second Opinion Use Case: The Pocket Professional
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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