Diagnostic Performance of Claude 3 from Patient History and Key Images in Diagnosis Please Cases

Abstract Backgrounds Large language artificial intelligence models have showed its diagnostic performance based solely on textual information from clinical history and imaging findings. However, the extent of their performance when utilizing radiological images and providing differential diagnoses has yet to be investigated. Purpose We employed the latest version of Claude 3, Opus, released on March 4, 2024, to investigate its diagnostic performance in answering Radiology’s Diagnosis Please quiz questions under three conditions: (1) when provided with clinical history alone; (2) when given clinical history along with imaging findings; and (3) when supplied with clinical history and key images. Furthermore, we evaluated the diagnostic performance of the model when instructed to list differential diagnoses. Materials and Methods Claude 3 Opus was tasked with listing the primary diagnosis and two differential diagnoses for 322 quiz questions from Radiology’s “Diagnosis Please” cases, which included cases 1 to 322, published from 1998 to 2023. The analyses were carried out under the following input conditions: Condition 1: Submitter-provided clinical history (text) alone Condition 2: Submitter-provided clinical history and imaging findings (text) Condition 3: Submitter-provided clinical history (text) and key images (PDF files) We applied McNemar’s tests to evaluate differences in correct response rates for primary diagnoses across Conditions 1, 2, and 3. Results The correct primary diagnoses rates were 62/322 (19.3%), 178/322 (55.3%), and 93/322 (28.8%) for Conditions 1, 2, and 3, respectively. Additionally, Claude 3 Opus accurately provided the correct answer as a differential diagnosis in up to 22/322 (6.8%) of cases. There were statistically significant differences in correct response rates for primary diagnoses between all combinations of Conditions 1, 2, and 3 (p<0.001). Conclusion Claude 3 Opus demonstrated significantly improved diagnostic performance by inputting key images in addition to clinical history. The ability to list important differential diagnoses was also confirmed. Key Results This study investigated Claude 3 Opus’s performance in Radiology Diagnosis Please Cases using clinical history, key images, and imaging findings. Key images or imaging findings inputs significantly improved correct primary diagnoses from 19.3% to 28.8% or 55.5%, respectively. By having two additional differential diagnoses presented, total correct responses improved by 3.1–6.8%. Summary statement Large language AI model Claude 3 Opus demonstrated significantly improved diagnostic accuracy by adding key images with clinical history compared with clinical history alone.