Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications

A new multi-agent AI framework designed for healthcare settings uses deterministic safety gates to curb premature diagnostic handoffs and silent clinical hallucinations, according to research published on arXiv. The system improved diagnostic precision by 11.3 percentage points over an unconstrained baseline in simulated tests. The framework, proposed by Divyansh Srivastava, targets two failure modes common in open-ended conversational agents powered by large language models: handing off a diagnosis before gathering sufficient information and generating plausible but incorrect clinical content that escapes detection [1][2]. Large language models, which are trained on vast text corpora through self-supervised learning, have shown promise for medical reasoning but remain vulnerable to these errors [7]. The architecture replaces “LLM-as-a-judge” routing with deterministic orchestration constraints [2]. A neuro-symbolic state-tracking gate enforces completeness of the OLDCARTS clinical protocol — covering Onset, Location, Duration, Character, Aggravating or Alleviating factors, Radiation, Timing, and Severity — by blocking diagnostic transitions until all required dimensions are collected [2]. A second mechanism, an epistemic uncertainty quantification gate, computes semantic entropy across five independent diagnostic samples to intercept divergent outputs before they reach a patient [2]. Evaluated on 150 test cases using simulated patient agents powered by the llama-3.1-70b-instruct model, the full architecture achieved 49.3% diagnostic precision, an absolute improvement of 11.3 percentage points over an unconstrained baseline [2]. The study also reported a statistically significant negative correlation between OLDCARTS completeness and semantic entropy (r = -0.181, p < 0.05), indicating that structured information gathering is associated with reduced diagnostic uncertainty [2]. The paper appears on arXiv, a preprint repository that has integrated with platforms such as Hugging Face Spaces to make machine learning research more accessible through interactive demos [3][4]. Through that integration, users can find open-source demos linked directly from a paper’s abstract page, allowing broader audiences to explore model behavior without writing code [5]. The underlying llama-3.1-70b-instruct model used in the evaluation belongs to a class of open-weight models that share parameters openly while keeping training data under restricted license [6][7]. Retrieval-augmented generation, a technique introduced in a 2020 paper by researchers including Douwe Kiela, who now serves as a research scientist director at Google DeepMind, has been one approach to grounding language model outputs in external knowledge [8]. The new framework takes a different path, using protocol enforcement and uncertainty sampling to constrain agent behavior directly [2].