DiagnoLLM: Transparent Diagnostic Inference

• DiagnoLLM is a class of diagnostic systems that integrate large language models with structured, dual-inference pipelines to ensure transparent, evidence-centric differential diagnoses.
• It employs a dual-inference framework combining forward and backward reasoning to iteratively validate candidate diagnoses with explicit rationales.
• The approach enhances diagnostic accuracy and interpretability through expert-annotated datasets, robust prompt engineering, and iterative feedback mechanisms.

DiagnoLLM refers to the emerging class of diagnostic systems that integrate LLMs with structured, interpretable pipelines for decision support, differential generation, and risk prediction in medical, industrial, and other complex domains. Contemporary DiagnoLLM frameworks combine prompt-based or hybrid neural reasoning with explicit evidence attribution, causal graph structures, or numerical calibration to produce not only ranked candidate diagnoses, but also transparent rationales tailored to clinicians and other expert end users.

1. Foundational Principles

DiagnoLLM systems are grounded in the necessity of interpretability, bidirectional reasoning, and evidence-centric explanations for differential diagnosis. The earliest models approached DDx as pure text classification via LLMs, but recent advances focus on dual-inference frameworks to ensure that every diagnosis is justified by both forward (symptoms→diagnoses) and backward (diagnoses→symptoms) reasoning. The use of multi-step prompt chaining, confidence thresholds, and explicit rationale comparison underpin this architecture (Zhou et al., 10 Jul 2024). LLMs are not fine-tuned in a gradient-based fashion but orchestrated via carefully structured prompting modules and iterative feedback loops.

2. Dual-Inference Framework for Differential Diagnosis

The Dual-Inf framework is a canonical instantiation:

• Forward inference F maps a clinical vignette $x$ to a set of candidate diagnoses $D_0$ and their supporting rationales $R_0$.
• Backward inference B takes each $d \in D_0$ and generates prototypical findings $S_d$ that would support $d$, independent of $x$.
• Examination module E compares $R_0$ against $S_d$ and the actual note, discards diagnoses with $|R_0(d)| < \beta$ (confidence threshold), and updates rationales.
• Turn-back mechanism prompts F to reconsider low-confidence items, iteratively up to a fixed $T$.
• Prompt design employs natural-language tasks, e.g., "list the top 5 possible diagnoses and provide supporting symptoms," with cascading context and no special tokenization.

Mathematically, the pipeline operates entirely via chain-of-prompt logic: $$D_0, R_0 \leftarrow F(x) \ \{ S_d \} \leftarrow B(D_0) \ D_1, R_1 \leftarrow E(x, D_0, R_0, \{ S_d \}, \beta)$$ Iterate until stable or $T$ exceeded.

3. Dataset Construction and Annotation Strategies

Modern DiagnoLLM research emphasizes high-quality, expert-annotated data. In the reference paper, raw data are drawn from USMLE-style question banks and clinical vignettes, de-duplicated, and filtered for minimal informational content (character count). Clinical experts annotate four dimensions: vignette, ground-truth differential diagnoses, granular reasons for each differential (mean 3.1 per diagnosis), and specialty label. Discrepancy resolution by consensus ensures reliability. The dataset size (N=570) is modest, but each entry is information-rich, supporting robust evaluation of both prediction and rationale quality.

4. Model Variants, In-Context Learning, and Prompt Engineering

DiagnoLLM systems leverage high-capacity, domain-general LLMs (e.g., GPT-4, GPT-4o) orchestrated with in-context exemplars (n=10) for prompt development. Competing approaches include:

• CoT (Chain-of-Thought) prompting: sequential, transparent reasoning.
• Diagnosis-CoT: diagnosis-focused step-by-step explanations.
• SC-CoT (Self-Consistency CoT): aggregate multiple independent CoT samples for stability.

Dual-Inf utilizes three prompt modules (F, B, E) plus the turn-back loop, all on the same LLM. No gradient fine-tuning or domain-specialized training is applied; performance relies on prompt robustness and architectural design.

5. Evaluation Metrics and Error Analysis

Evaluation incorporates both automated and human metrics:

• Diagnosis accuracy: fraction of ground-truth differentials predicted.
• Interpretation accuracy: correctness of rationale per diagnosis, judged by both automated metrics (BERTScore, SentenceBert cosine similarity, METEOR) and blinded clinician assessment.
• Statistical significance: Paired comparisons across 5 runs (random-seed stratification), reporting 95% CIs; improvements are robust ($p < 0.001$).

Error taxonomy is detailed: missing content, factual error, and weak relevance. Dual-Inf reduces content omission by ~13% and halves factual errors compared to self-consistency baselines. On rare disease cases, interpretation metrics improve by >10%.

6. Interpretation Mechanisms and Error Reduction

Interpretation quality is enabled by backward module recall ($S_d$ as gold-standard for each candidate diagnosis) and iterative feedback ("turn-back"). This mechanism cross-validates evidential claims against both annotated symptom findings and best-practice medical knowledge, systematically pruning unsupported hypotheses. The collaborative interaction of F, B, and E modules enables hallucination mitigation: diagnoses lacking robust support are filtered, and rationales are forced to align with recognized clinical findings.

Case studies confirm improved capture and explanation of non-obvious diagnoses (e.g., correct triad assignment in trauma cases), with richer, more complete reasons cited for each differential. The approach is particularly beneficial in rare disease settings, where conventional LLM prompting fails to produce adequate support.

7. Limitations and Prospective Directions

Current DiagnoLLM pipelines are constrained by:

• Limited dataset size and specialty coverage (9 domains).
• Lack of multimodal integration (laboratory, imaging data).
• Dependency on proprietary APIs and prompt heuristics ($\beta$, $T$).
• Absence of real-world EHR complexity and longitudinal patient history.

Future research prioritizes:

• Scaling to full-spectrum EHRs and continuous history.
• Incorporation of dynamic decision modules (e.g., lab test ordering).
• Use of open-source LLMs with domain fine-tuning and reinforcement/bandit optimization of prompt parameters.
• Integration of chain-of-thought reasoning with retrieval-augmented knowledge bases and clinical guidelines.
• Unified frameworks for multimodal (text, image, structured) diagnostic inference and explanation.

8. Significance and Generalization

DiagnoLLM, as exemplified by Dual-Inf and related architectures, demonstrates that a prompt-centric, bidirectionally constrained inference pipeline significantly enhances both differential accuracy and rationale faithfulness without model parameter tuning. This advancement bridges the gap between automated diagnosis and interpretable clinical reasoning. The paradigm generalizes beyond medicine: similar mask-ask-unmask and graph-driven diagnostic probing strategies have demonstrated efficacy in legal (Wu et al., 5 Jun 2024), engineering (Dave et al., 8 Feb 2024), and industrial settings (Tao et al., 5 Nov 2024, Lee et al., 27 Sep 2025).

A plausible implication is that DiagnoLLM architectures, when extended with domain adaptation, multimodal capabilities, and calibrated uncertainty estimation, will become foundational infrastructure for interpretable, trustworthy decision-support across expert-facing domains.