Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.

BACKGROUND

Artificial intelligence (AI) has achieved good performance in image recognition, including identifying cancer cells in pathology images. However, the best mode of AI assistance in diagnostic pathology remains to be explored.

METHODS

We compared the influence of two deep-learning assistance modes on pathologists in cancer identification. Ten board-certified pathologists classified 60 cases of nasopharyngeal biopsy as carcinoma or benign with or without AI assistance, which was either a heatmap of cancer probability accompanying a whole-slide image (AI-heatmap mode) or ten high-power field images with the highest cancer probability (AI-HPF mode).

RESULTS

Both assisting modes significantly accelerated the diagnostic process, lowered the subjective difficulty, and maintained high accuracy compared to the unassisted mode. Notably, the acceleration of diagnosis was more significant in AI-HPF mode than in AI-heatmap mode (time reduction: 35.1% vs. 28.1%; P = 0.040), especially for benign cases (time reduction: 49.4% vs. 32.9%; P = 0.0000072). For benign cases, an increased area proportion of false-positive AI prediction slowed down the diagnostic process in AI-heatmap mode (P = 0.00000084) but not in AI-HPF mode (P = 0.62).

CONCLUSIONS

We show for the first time that an AI-HPF assistance mode was superior to the commonly used AI-heatmap mode in accelerating cancer identification by pathologists. In our scenario, the AI-HPF mode maintained high diagnostic accuracy and was robust to the influence of false-positive AI prediction. The potential risk caused by AI assistance is also discussed.