Conversational Disease Diagnosis via External Planner-Controlled Large Language Models

Core Concepts

This study introduces an innovative approach using external planners augmented with large language models (LLMs) to develop a medical task-oriented dialogue system for conversational disease diagnosis. The system comprises a policy module for information gathering, and an LLM-based module for natural language understanding and generation, addressing the limitations of previous AI systems in these areas.

Abstract

This study proposes an external planner-controlled LLM system to achieve conversational disease diagnosis. The key highlights are: The system emulates the two-phase decision-making process of doctors - disease screening and differential diagnosis. It implements two distinct planners to handle these two phases. For disease screening, the first planner uses reinforcement learning and active learning with LLMs to effectively inquire about patient symptoms and identify potential high-risk diseases. For differential diagnosis, the second planner integrates evidence-based medical knowledge from clinical guidelines, transforming them into structured diagnostic decision processes. This allows the LLM to rigorously follow the procedures to confirm or rule out the high-risk diseases. Evaluation on the MIMIC-IV dataset shows the system outperforms existing models in both disease screening and differential diagnosis, indicating a significant step towards automated conversational disease diagnostics. The study demonstrates the feasibility of using open-source LLMs like Llama2 combined with the external planners to achieve diagnostic capabilities comparable to commercial LLMs like GPT-4.

Stats

The top 1 hit rate (where the primary diagnosis of a patient is ranked at the highest risk) is 0.34, surpassing the performance of a purely GPT-4 turbo-based doctor simulator (0.30). In the differential diagnosis of heart failure, the F1 score is over 90%, suggesting the system can effectively diagnose diseases pinpointed in the first stage.

Quotes

"Enabling conversational diagnosis has been a long-awaited goal in medical artificial intelligence." "The emergence of large language models (LLMs) has brought unprecedented opportunities in implementing conversational diagnosis." "We contend that the proposed AI diagnostic system has the potential to enhance diagnostic precision and accessibility."

Key Insights Distilled From

Conversational Disease Diagnosis via External Planner-Controlled Large Language Models

by Zhoujian Sun... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04292.pdf

Deeper Inquiries

Potential Challenges in Scaling the Approach

Scaling the approach to cover a broader range of diseases beyond the 98 included in the study may present several challenges. One significant challenge is the need for a more extensive and diverse dataset to train the AI system effectively. The MIMIC-IV dataset, while comprehensive, may not encompass all possible variations and complexities of different diseases. Therefore, acquiring and integrating data from various sources to ensure the model's robustness and accuracy across a wider spectrum of diseases would be crucial. Another challenge is the complexity of differential diagnosis for a broader range of diseases. Each disease may have unique symptoms, risk factors, and diagnostic criteria, making it challenging to develop specific decision procedures for each condition. Ensuring that the system can accurately differentiate between a wide array of diseases would require extensive knowledge and expertise in various medical specialties. Moreover, the interpretability and explainability of the AI system's decisions become more critical as the number of diseases increases. Understanding how the system arrives at a diagnosis for a vast range of conditions is essential for gaining trust from healthcare professionals and patients. Ensuring transparency in the decision-making process and providing clear justifications for the diagnoses become more challenging as the complexity of the diseases increases.

Improving System Performance with Additional Medical Data Sources

Incorporating additional medical data sources beyond the MIMIC-IV dataset can significantly enhance the system's performance in several ways. One key aspect is the inclusion of real-time patient data from electronic health records (EHRs) and other healthcare databases. By integrating live patient data, the AI system can adapt to individual patient profiles, track disease progression, and consider the latest test results and treatment outcomes in its diagnostic process. Furthermore, leveraging data from wearable devices and remote monitoring tools can provide valuable insights into patients' daily health metrics, lifestyle factors, and environmental influences. Integrating this real-time data into the AI system can enable more personalized and proactive healthcare interventions, leading to more accurate and timely diagnoses. Collaborating with healthcare institutions and research organizations to access a diverse range of medical data, including genetic information, biomarkers, and treatment histories, can also enrich the system's knowledge base. By continuously updating and expanding the dataset with new and relevant information, the AI system can stay current with the latest advancements in medical research and clinical practice, ultimately improving diagnostic accuracy and patient outcomes.

Ethical Considerations in Deploying the AI System

Deploying an AI system for medical diagnosis in real-world clinical settings raises several ethical considerations that must be carefully addressed. One primary concern is patient privacy and data security. Ensuring the confidentiality and protection of patient health information is paramount, especially when dealing with sensitive medical data. Implementing robust data encryption, access controls, and compliance with healthcare regulations such as HIPAA is essential to safeguard patient privacy. Another ethical consideration is the transparency and accountability of the AI system's decisions. Healthcare professionals and patients should have a clear understanding of how the AI system operates, the basis for its recommendations, and the limitations of its diagnostic capabilities. Providing explanations for the system's decisions in a comprehensible manner can help build trust and facilitate collaboration between AI technology and human healthcare providers. Additionally, bias and fairness in the AI system's algorithms must be carefully monitored and mitigated to prevent discriminatory outcomes. Ensuring that the AI system is trained on diverse and representative datasets, regularly auditing its performance for bias, and implementing mechanisms for bias correction and fairness testing are essential steps in promoting equitable healthcare delivery. Moreover, healthcare professionals should be adequately trained in using and interpreting the AI system's outputs to prevent overreliance on automated diagnoses and maintain the human touch in patient care. Establishing clear guidelines for the responsible use of AI technology in clinical practice and fostering a culture of shared decision-making between AI systems and healthcare providers can uphold ethical standards and ensure patient safety and well-being.