Tasks

Hidden-RAD2 consists of two main subtasks. Participants can choose to participate in one or both tasks.

Task 1: Structured Radiological Reasoning Generation

• Background: Radiologists interpret chest X-ray images through a structured process, progressing from initial observations to final impressions and confirmation of the supporting evidence. This task asks AI systems to reproduce this diagnostic reasoning process explicitly.

• Input:

  1. A chest X-ray image from the MIMIC-CXR dataset

  2. (Optional) external medical knowledge resources

• Output: A structured set of answers representing the radiologist’s reasoning process: initial impressions (A1), thoracic levels (A2), anatomical locations (A3), final impressions (A4), and an ABCDE-based confirmation checklist (A5).
  
  

• For further details, see the Task 1 Definition.

Task 2: Causal Verification and Correction

• Background: While Large Language Models (LLMs) generate fluent text, they can produce "hallucinations" that are disconnected from facts. In the medical field, such errors can be critical. This task is designed to evaluate an AI's ability to self-verify the reliability of its generated text and correct errors. This "critical self-review" capability is essential for ensuring the safety and transparency of AI systems.

• Input:

1. The original report and image (optional)

2. An AI-generated explanation that contains seeded errors.

• Output:

1. Error Detection: Identify the location and type of errors within the explanation (e.g., flawed causality, factual inconsistency).

2. Error Correction: Correct the identified errors with accurate information.

3. Confidence Score: Provide a confidence score (from 0 to 1) for the overall correctness of the explanation.

• For further details, see the Task 2 Definition.