RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

1. 1
   Identify Interpretability Requirements

   Pinpoint high-stakes domains (e.g., medical, legal, finance) where AI decisions demand explicit human understanding, trust, and validation. Focus on scenarios where 'black-box' outputs are unacceptable.
2. 2
   Architect Tool-Using Agents

   Design AI agents capable of interacting with external tools (e.g., image processing libraries, databases, diagnostic models). Ensure the agent can select and execute relevant tools based on its current task and context.
3. 3
   Implement Stepwise Reasoning Traces

   Develop the agent to log its decision-making process at each significant step. This includes recording tool calls, intermediate results, and the rationale for moving to the next stage, creating a transparent reasoning trace.
4. 4
   Enable Human Oversight and Correction

   Integrate mechanisms that allow human experts to inspect the agent's reasoning trace. Design interfaces for validating individual steps, identifying potential errors, and providing corrective feedback to guide the agent.
5. 5
   Prioritize Human-AI Collaboration

   Structure the agent's output and interaction flow to facilitate active collaboration. Ensure human experts can easily verify, refine, and ultimately take ownership of the AI's conclusions, fostering trust and effective integration into real-world workflows.

class InterpretableAgent:
    def __init__(self, name="MyAgent"):
        self.name = name
        self.reasoning_trace = []
        self.tools = {
            "image_analyzer": self._mock_image_analyzer,
            "data_fetcher": self._mock_data_fetcher
        }

    def _mock_image_analyzer(self, image_data):
        # Simulate an external tool call
        result = f"Analyzed image data: {image_data[:20]}... Detected 'abnormal' features."
        return result

    def _mock_data_fetcher(self, query):
        # Simulate fetching data from a knowledge base
        result = f"Fetched data for query '{query}': 'Relevant clinical guidelines found.'"
        return result

    def execute_task(self, task_input):
        self.reasoning_trace.append(f"[{self.name}] Starting task with input: '{task_input}'")

        # Step 1: Use a tool to analyze input
        analysis_result = self.use_tool("image_analyzer", task_input)
        self.reasoning_trace.append(f"[{self.name}] Step 1: Used 'image_analyzer'. Result: {analysis_result}")

        # Step 2: Based on analysis, fetch more data
        if "abnormal" in analysis_result:
            data_query = "chest CT abnormalities"
            data_result = self.use_tool("data_fetcher", data_query)
            self.reasoning_trace.append(f"[{self.name}] Step 2: Used 'data_fetcher'. Result: {data_result}")
            final_conclusion = f"Potential abnormality detected. Consulted guidelines. {data_result}"
        else:
            final_conclusion = "No significant abnormalities detected."

        self.reasoning_trace.append(f"[{self.name}] Final Conclusion: {final_conclusion}")
        return final_conclusion

    def use_tool(self, tool_name, *args, **kwargs):
        if tool_name in self.tools:
            tool_output = self.tools[tool_name](*args, **kwargs)
            self.reasoning_trace.append(f"    -> Tool '{tool_name}' executed with args {args}. Output: {tool_output[:50]}...")
            return tool_output
        else:
            raise ValueError(f"Tool '{tool_name}' not found.")

    def get_reasoning_trace(self):
        return "\n".join(self.reasoning_trace)

# Example Usage:
agent = InterpretableAgent("RadAgent_Concept")
agent.execute_task("raw_chest_ct_scan_data_XYZ123...")
print(agent.get_reasoning_trace())

• Paperarxiv.org