Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis
arXiv:2604.05116v1 Announce Type: new Abstract: Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths. To this end, we formulate sequential diagnosis as a Latent Diagnostic Trajectory Learning (LDTL) framework based on a planning LLM agent and a diagnostic LLM agent. For the diagnostic LLM agent, diagnostic action sequences are treated as latent paths and we introduce a posterior distribution that prioritizes trajectories providing mo
arXiv:2604.05116v1 Announce Type: new Abstract: Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths. To this end, we formulate sequential diagnosis as a Latent Diagnostic Trajectory Learning (LDTL) framework based on a planning LLM agent and a diagnostic LLM agent. For the diagnostic LLM agent, diagnostic action sequences are treated as latent paths and we introduce a posterior distribution that prioritizes trajectories providing more diagnostic information. The planning LLM agent is then trained to follow this distribution, encouraging coherent diagnostic trajectories that progressively reduce uncertainty. Experiments on the MIMIC-CDM benchmark demonstrate that our proposed LDTL framework outperforms existing baselines in diagnostic accuracy under a sequential clinical diagnosis setting, while requiring fewer diagnostic tests. Furthermore, ablation studies highlight the critical role of trajectory-level posterior alignment in achieving these improvements.
Executive Summary
The article introduces a novel Latent Diagnostic Trajectory Learning (LDTL) framework for sequential clinical diagnosis, addressing the challenge of acquiring clinical evidence under uncertainty. Unlike traditional LLM-based diagnostic systems that assume fully observed patient information, LDTL models evidence acquisition as a sequential decision process, treating diagnostic action sequences as latent paths. The framework employs two LLM agents—a diagnostic agent and a planning agent—to prioritize trajectories that maximize diagnostic information while minimizing uncertainty. Evaluated on the MIMIC-CCM benchmark, LDTL demonstrates superior diagnostic accuracy and efficiency compared to existing baselines, reducing the need for excessive diagnostic tests. Ablation studies underscore the importance of trajectory-level posterior alignment in achieving these outcomes, highlighting the framework’s potential to enhance clinical decision-making in real-world settings.
Key Points
- ▸ Sequential clinical diagnosis is reframed as a latent trajectory learning problem, departing from conventional approaches that assume full patient information.
- ▸ The LDTL framework introduces a posterior distribution to prioritize diagnostic paths that maximize information gain, enabling more efficient and accurate sequential decision-making.
- ▸ Experiments on the MIMIC-CCM benchmark show LDTL outperforms baselines in diagnostic accuracy while requiring fewer tests, with ablation studies confirming the critical role of trajectory-level posterior alignment.
Merits
Innovative Framework for Uncertainty-Aware Diagnosis
The LDTL framework explicitly models uncertainty in sequential clinical diagnosis, addressing a critical gap in LLM-based diagnostic systems that often assume fully observed patient data.
Efficiency and Accuracy Improvements
Empirical validation on the MIMIC-CCM benchmark demonstrates that LDTL achieves higher diagnostic accuracy with fewer tests, offering a scalable solution for real-world clinical settings.
Robust Theoretical Foundation
The use of a posterior distribution to guide trajectory learning provides a mathematically sound approach to prioritizing high-information diagnostic paths, enhancing the framework’s reliability.
Demerits
Limited Generalizability to Non-Sequential Diagnoses
The framework is tailored for sequential clinical diagnosis; its applicability to non-sequential or acute diagnostic scenarios may be limited, potentially restricting its utility in broader clinical contexts.
Dependence on High-Quality Benchmark Data
The performance of LDTL is contingent on the quality and representativeness of training data (e.g., MIMIC-CCM), which may introduce biases or limitations inherent to the dataset.
Computational Complexity
The dual-agent architecture and posterior alignment mechanisms may introduce computational overhead, posing challenges for deployment in resource-constrained clinical environments.
Expert Commentary
The LDTL framework represents a significant advancement in the application of LLMs to sequential clinical diagnosis, addressing a longstanding challenge in medical AI: the explicit modeling of uncertainty in evidence acquisition. By framing diagnosis as a latent trajectory learning problem, the authors elegantly bridge the gap between theoretical rigor and practical utility. The dual-agent architecture—comprising a diagnostic and planning agent—is particularly innovative, as it mirrors the dual-process theory of clinical reasoning (intuitive vs. analytical). However, the reliance on posterior alignment for trajectory prioritization introduces a layer of complexity that may require further validation in diverse clinical settings. While the results on the MIMIC-CCM benchmark are promising, the framework’s generalizability to non-sequential or acute care scenarios remains an open question. Moreover, the computational demands of the system could pose barriers to adoption in resource-limited healthcare systems. Nonetheless, LDTL sets a new standard for uncertainty-aware diagnostic AI and underscores the potential of latent trajectory learning in transforming clinical decision-making.
Recommendations
- ✓ Conduct prospective clinical trials to validate the LDTL framework in real-world settings, ensuring its applicability across diverse patient populations and healthcare systems.
- ✓ Explore hybrid approaches that combine LDTL with rule-based systems or clinician-in-the-loop mechanisms to enhance interpretability and trust in AI-driven diagnoses.
- ✓ Investigate the framework’s adaptability to non-sequential diagnoses (e.g., emergency medicine) and its potential integration with other AI modalities (e.g., computer vision for imaging data).
- ✓ Develop standardized evaluation metrics for uncertainty quantification in clinical AI, enabling fairer comparisons across frameworks and facilitating regulatory approval.
Sources
Original: arXiv - cs.AI