Libra - Temporal Insight 🕰️

What Does “Temporal” Really Mean?

In clinical radiology, temporal information is not just about “past” and “present” — it’s about change. When radiologists assess a chest X-ray, they’re not merely describing what they see in a single image; they’re often comparing it to a previous one to identify whether a patient’s condition has improved, worsened, or remained stable.

🔔 This kind of temporal reasoning is essential in everyday medical practice. Yet most multimodal large language models (MLLMs) either ignore it or fail to model it effectively.

Time Tells the Truth: Interpreting Temporal Changes in Imaging

1. Macro-Level Progression

• Is the patient improving, deteriorating, or stable?

Macro-level comparison focuses on the overall trajectory of the patient’s condition compared to prior examinations. This high-level temporal reasoning is crucial for tracking disease evolution and guiding clinical decision-making.

2. Lesion-Specific Temporal Changes

• How are individual abnormalities evolving over time?

Fine-grained analysis captures precise changes in specific findings, such as “consolidation in the left lower lobe has significantly expanded” or “cardiac silhouette shows no appreciable change.” These insights enable clinicians and models to reason at the level of targeted anatomical and pathological detail.

3. Quality Over Quantity in Temporal Inputs

• ❗️ Adding more images often introduces noise and computational complexity without improving diagnostic value.

Clinical Applications

From Diagnostic Judgement to Multi-Scale Temporal Understanding

Temporal Reasoning in Clinical Diagnosis

Radiologists rarely analyse a chest X-ray in isolation. Instead, they routinely ask:

• “Has the consolidation improved since last week?”
• “Is the pleural effusion new?”
• “Has the cardiac silhouette changed?”

Such reasoning typically falls into three primary categories:

• Improved: Lesions have shrunk or resolved.
• Worsened: New abnormalities appear, or existing ones have grown.
• Stable: No meaningful change is observed.

These are coarse-level temporal descriptions. On a finer level, radiologists describe:

• How much a lesion has changed in size or density,
• Whether opacities have shifted,
• If tubes, lines, or devices have been added or removed.

Temporal Signals Span Multiple Scales

Temporal information in radiology is inherently multi-scale — ranging from global clinical trajectories to subtle, localised anatomical changes.

- Temporal Information in Radiology
  - Clinical Categories
    - Improved
      - Lesions have shrunk
      - Opacities decreased
      - Inflammatory shadows reduced
    - Worsened
      - New abnormalities appeared
      - Existing lesions grown
      - New infiltrates or effusion
    - Stable
      - No meaningful change
      - Chronic conditions
      - Continuous monitoring needed
  - Information Levels
    - Macro-level trends
      - Overall patient trajectory
      - Global comparison
    - Local lesion changes
      - Size changes
      - Density variations
      - Positional shifts
  - Clinical Applications
    - Triage decisions
      - Emergency prioritization
      - Resource allocation
    - Treatment evaluation
      - Response assessment
      - Therapy adjustment
    - Long-term monitoring
      - Chronic disease management
      - Post-surgical follow-up

Triage and Resource Allocation

Prioritising Care When Every Minute Counts

Clinical Goals and Operational Pressures

Chest X-rays play a pivotal role in patient triage, especially in emergency and high-volume settings. Radiologists must rapidly determine:

• Which patients require immediate intervention,
• Who can safely wait,
• And how to allocate limited resources most effectively.

Triage is fundamentally about maximising outcomes under constraint. The goal is not to fully characterise every patient’s history, but to make fast, high-impact decisions that ensure critical cases receive timely care—without neglecting those with non-urgent needs.

What Matters Most: Clinically Significant Change

In these time-sensitive settings, timeliness and diagnostic clarity outweigh completeness. Radiologists focus on:

• New acute findings: Abnormalities not previously seen that may indicate emerging crises.
• Significant deterioration: Rapid worsening of known conditions that may demand escalated care.
• Stable chronic findings: Ongoing issues that show no meaningful progression and can be managed routinely.

Our Model Approach

Temporal Efficiency Aligned with Clinical Reasoning

Why Two Images Are Sufficient

In real-world radiology workflows, the most informative temporal comparison is typically between:

1. The current chest X-ray, and
2. The most recent prior image used for diagnosis.

While patients may have a rich archive of historical scans, only the immediately preceding diagnostic image provides the relevant baseline for interpreting new findings. Additional older images may support longitudinal studies, but they often introduce noise, redundancy, and delay in fast-paced clinical decision-making.

Libra’s Design Philosophy: Focused Temporal Reasoning

Libra is built on this clinically grounded principle.
Instead of processing full temporal sequences — which can be computationally expensive and semantically ambiguous — Libra learns to model directional change between two key time points.

This design enables the model to:

• Mimic the focused comparison strategies of expert radiologists,
• Avoid temporal noise from irrelevant or outdated scans,
• Reduce computational load while preserving diagnostic fidelity.

In short, Libra treats temporal reasoning as radiologists do:
What’s changed since the last meaningful image?

Illustrations

To better understand how radiologists and Libra approach temporal comparison, we present two illustrative workflows: a general conceptual flow and a specific clinical case.

Conceptual Workflow

The following diagram outlines the reasoning pathway taken when comparing a current chest X-ray to the most recent prior image. Based on observed changes (or lack thereof), radiologists infer the clinical trajectory and guide downstream decisions.

Case Example: Lung Consolidation

This diagram demonstrates a practical example: a patient with lung consolidation. Depending on the direction of change, clinical interpretation and management decisions vary significantly.

AI Model Implications

Why Most MLLMs Fall Short — and How Libra Goes Further

Many multimodal large language models (MLLMs) struggle with temporal reasoning for three key reasons:

• No awareness of time: They treat images independently and ignore their chronological order.
• Hallucinated references: They fabricate prior findings without reliable comparison.
• Lack of temporal alignment: They have no built-in mechanism to align or contrast image features across time.

Libra tackles these issues head-on.

Instead of prompting the model to “guess” what might have changed, Libra incorporates explicit temporal awareness into both its architecture and training process.

We feed the model structured, temporally aligned visual features extracted from the current and previous images. This enables Libra to:

• Detect fine-grained, clinically meaningful changes,
• Avoid hallucination,
• And reason about progression or stability in a way that mirrors clinical thinking.

In the next section, Libra – Structural Logic 🧠, we’ll explore how Libra’s architecture is intentionally designed to reflect clinical reasoning. You’ll see how its modular structure enables it to reason across both time and image features with precision — building on the foundation established in Libra – Temporal Insight 🕰️.