The paper introduces Uncertainty-Guided Latent Diagnostic Trajectory Learning (LDTL), a framework that uses a planning LLM agent and a diagnostic LLM agent to model sequential clinical diagnosis under uncertainty. By treating diagnostic action sequences as latent paths and applying a posterior distribution that favors trajectories with higher diagnostic value, the approach encourages coherent evidence acquisition that progressively reduces uncertainty. Experiments on the MIMIC-CDM benchmark show that LDTL improves diagnostic accuracy while requiring fewer tests, and ablation studies confirm the importance of trajectory-level posterior alignment.
arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquire...
ActiveMedAgent is a cost‑aware framework that guides multimodal medical AI to acquire diagnostic evidence sequentially, mirroring clinical practice of escalating from cheap to costly tests. It uses a frozen vision‑language model to track diagnosis probabilities, scores each potential acquisition by diagnostic utility minus cost, and trains a lightweight MLP controller offline to decide when to request more evidence or to stop. Experiments on three benchmarks show that trajectory‑based policy learning outperforms unguided acquisition and full‑modality baselines, revealing an information overload effect where the agent can correctly diagnose with fewer channels than the full‑modality approach.
ConsultMind is an uncertainty‑aware framework that automates diagnostic consultation by updating disorder posteriors after each patient response and using posterior uncertainty to guide inquiry and diagnosis. It builds on AutoDisym, a pipeline that constructs a Disorder–Symptom Bayesian Network (DSBN) from diagnostic knowledge and clinical narratives. Across psychiatry, respiratory medicine, fever clinics, and public datasets, AutoDisym produces high‑quality DSBNs and ConsultMind improves diagnostic accuracy and explanation quality, achieving up to 22.15‑point gains in Top‑1 accuracy and 37.89‑point gains in Top‑3 accuracy.
arXiv:2607. 15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering.
arXiv:2610.34878v1 Announce Type: new Abstract: Disease diagnosis is a key application of large language model (LLM) agents in healthcare. However, existing medical agents are primarily trained and e...
EviDx is a new framework for evidence-aware active diagnosis that pairs patient-specific diagnostic environments with a clinical scaffold and an observer-guided runtime harness. The framework constructs interactive environments from raw clinical cases, organizes role-specialized agents and evidence tools, and regulates diagnostic termination by tracking uncertainty and evidence coverage. Experiments demonstrate that EviDx improves diagnostic performance and process stability while revealing model-dependent capability boundaries.
Disease diagnosis is a key application of large language model (LLM) agents in healthcare. However, existing medical agents are primarily trained and evaluated with static question-answering data, pro...
arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
STRIVE is a new framework for longitudinal radiology report generation that separates clinical reasoning into distinct Diagnosis, Attribute, and Temporal Change agents, each producing explicit evidence. The Temporal Change agent is refined with a Progression-Aware GRPO reward that differentiates direction-preserving errors from reversals. Verification occurs twice: a Consistency Gate aligns agent outputs before report generation, and a Validation Agent ensures the final report is supported by the aggregated evidence. On the Longitudinal-MIMIC dataset, STRIVE achieves superior clinical efficacy and more than doubles Longitudinal Change Concordance compared to the strongest baseline.
arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect pati...
The paper introduces MedAction, a tree‑structured distillation pipeline that generates high‑quality multi‑turn diagnostic trajectories by interacting an LLM with an environment. It identifies three failure modes in current LLMs—ungrounded test ordering, unreliable diagnostic updates, and degraded multi‑turn coherence—and proposes two knowledge‑graph‑grounded metrics, Disease Trajectory Consistency and Reasoning‑Action Consistency, to filter trajectory quality. Using this pipeline, the authors create MedAction‑32K, a dataset of 32,681 trajectories from 2,896 PMC cases, and fine‑tune an 8B model that achieves state‑of‑the‑art performance on MedR‑Bench and the MedAction‑300‑Hard benchmark among open‑source medical LLMs.
arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.