๐Ÿš€ New Preprint from the HACID Project!ย 

25 June 2024

We are excited to share our latest findings in the preprint “Human-AI collectives produce the most accurate differential diagnoses.” This study demonstrates that human-AI collectives, combining human expertise with advanced AI models, significantly improve diagnostic accuracy.ย 

Key Highlights:

  • We analyzed 2,133 medical cases and 40,762 physician diagnoses from the Human Diagnosis Project to compare human-only, AI-only, and hybrid collectives. The combination of AI and physician expertise produces superior outcomes. ๐Ÿค๐Ÿค–๐Ÿง 
  • Our findings reveal that humans and AI make different types of errors, and their complementary strengths lead to higher diagnostic accuracy. When AI misses a diagnosis, humans often get it right, and vice versa. This synergy is crucial for better performance. ๐ŸฉบโŒ๐Ÿ’กโš–๏ธ

  • We utilized state-of-the-art large language models, including Anthropic Claude 3 Opus, Google Gemini Pro 1.0, Meta Llama 2 70B, Mistral Large, and OpenAI GPT-4, to diagnose the same medical cases as human doctors, aggregating their responses into collective diagnoses. ๐Ÿค–๐Ÿง ๐Ÿฉบ
  • Medical specialties such as cardiology, gastroenterology, and infectious diseases all benefited from this hybrid approach, highlighting the broad applicability and potential for improving diagnostic accuracy across various medical fields. ๐Ÿ“š
  • Using SNOMED CT healthcare terminology and advanced NLP techniques, we automatically harmonized and aggregated diagnoses from both humans and AI, eliminating the need for human intervention in this step. ๐Ÿ› ๏ธ๐Ÿ”„
  • Diagnostic errors cause nearly 795,000 deaths and permanent disabilities annually in the U.S. alone. Our approach aims to reduce these errors and improve patient outcomes without significantly increasing costs. ๐ŸŒ๐Ÿ“ˆ
  • We used case vignettes in text form for this study. Future research could explore integrating multimodal data and assessing performance in real-world clinical settings and across diverse populations, while addressing potential biases. ๐Ÿ”ฎ

Read the full preprint here: http://arxiv.org/abs/2406.14981


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HACID is an HORIZON Innovation Action, a collaborative project funded under the Horizon Europe Programme, within the topic โ€œAI, Data and Robotics at workโ€ (HORIZON-CL4-2021-DIGITAL-EMERGING-01-10).

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