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ARXIV:2603.01241 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.01241MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy.
Opportunity summary
Pain Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such…
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior example at the right reasoning step.…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experiments on medical question-answering benchmarks show consistent gains over strong medical RAG baselines and prompting-only reasoning methods.
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
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Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy.
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Paper Pack
10.48550/arXiv.2603.01241Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy.
Abstract
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior example at the right reasoning step. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such as guidelines, protocols, and pharmacologic mechanisms; and experience, verified reasoning trajectories from previously solved cases (e.g., chain-of-thought solutions and their step-level decompositions). At test time, the agent retrieves both relevant skills and experiences from curated libraries and performs lightweight test-time adaptation to align the language model's intermediate reasoning with clinically valid logic. Concretely, we build (i) a skills library from guideline-style documents organized as executable decision rules, (ii) an experience library of exemplar clinical reasoning chains indexed by step-level transitions, and (iii) a step-aware retriever that selects the most useful skill and experience items for the current case. We then adapt the model on the retrieved items to reduce instance-step misalignment and to prevent reasoning from drifting toward unsupported shortcuts. Experiments on medical question-answering benchmarks show consistent gains over strong medical RAG baselines and prompting-only reasoning methods. Our results suggest that explicitly separating and retrieving clinical skills and experience, and then aligning the model at test time, is a practical approach to more reliable medical agents.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 6.0
PROBLEM
Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such as guidel...
METHOD
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior example at the right reasoning step. We frame clinical question answering as an agent problem with two...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experiments on medical question-answering benchmarks show consistent gains over strong medical RAG baselines and prompting-only reasoning methods.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such as guidelines, protocols, and pharmacologic mechanisms; and experience, verified reasoning trajectories from previously solved cases (e.g., chain-of-thought solutions and their step-level decompositions).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior example at the right reasoning step. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such as guidelines, protocols, and pharmacologic mechanisms; and experience, verified reasoning trajectories from previously solved cases (e.g., chain-of-thought solutions and their step-level decompositions).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experiments on medical question-answering benchmarks show consistent gains over strong medical RAG baselines and prompting-only reasoning methods.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Enhance medical agents with retrieval-based adaptation of clinical skills and experiences for improved decision-making accuracy.
Segment
Medical AI
Adoption evidence
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Commercial read
6.0/10 public viability
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proof status
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Evidence coverage
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Technical feasibility
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Current read
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Evidence
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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