Performance of next-generation AI chatbots in colorectal cancer knowledge assessment: a comparative pilot study of...

As AI models evolve, their application in specialized fields like colorectal cancer requires rigorous validation. This pilot study aimed to comparatively assess the knowledge retention, safety, and reasoning limitations of six advanced AI chatbots using a constrained zero-shot multiple-choice question format. 137 text-based MCQs covering 12 core colorectal cancer modules were adapted from the 2023 Chinese guidelines and administered to Gemini 3 Pro Preview, GPT-5.1, Kimi K2 Thinking, DeepSeek V3.2, Qwen3-Max, and Claude Opus 4.5 under zero-shot conditions with a prompt prohibiting reasoning steps. Both quantitative statistical analysis and qualitative error analysis were performed. Overall accuracy was low: Kimi K2 Thinking 27.74%, SD = 0.45, Claude Opus 4.5 26.28%, SD = 0.44, Gemini 3 Pro 16.06%, SD = 0.37, DeepSeek V3.2 15.33%, SD = 0.36, GPT-5.1 14.60%, SD = 0.35, and Qwen3-Max 13.87%, SD = 0.34. Significant module-wise disparities emerged, with Kimi K2 scoring 37.04% in Endoscopic Imaging versus Qwen3-Max at 7.41%. Qualitative analysis revealed four failure patterns: semantic association bias, hierarchical logic failure, fact retrieval error, and hallucinations. No correlation existed between item difficulty and accuracy. Under constrained prompts, next-generation AI chatbots demonstrate unsatisfactory colorectal cancer performance, often relying on keyword matching rather than physiological simulation. This leads to dangerous clinical errors, highlighting the critical need for chain-of-thought prompting, expert oversight, and domain-specific fine-tuning before unsupervised use.