Table 1.

Summary of NLP studies focusing on actionable radiology reports (ML: Machine Learning, DL: Deep Learning, BERT: Bidirectional Encoding Representation of Transformer).

Author(s)	Language	Number of radiology reports	Algorithm	Section of report	Research objective
Carrodeguas et al. [33]	English	2306	ML/DL	Impression	Classifying recommendation
Helibrun et al. [34]	English	851	Rule-based	Impression	Detecting critical finding
Lou et al. [35]	English	6000	ML	Not mentioned	Classifying recommendation
Esteban et al. [36]	English	3401	Software	Findings, impression	Classifying recommendation
Morioka et al. [37]	English	1402	Rule-based	Not mentioned	Classifying disease condition
Fu et al. [38]	English	1000	Rule-based ML/DL	Not mentioned	Classifying disease condition
Nakamura et al. [39]	Japanese	63646	BERT	Order, findings, impression	Detecting critical finding
Jujjavarapu et al. [40]	English	871	ML	Not mentioned	Classifying disease condition
Liu et al.. [15]	Chinese	1089	BERT/ML	Findings	Classifying disease condition
Zhang et al. [41]	Chinese	359	BERT Pre-training	Findings	Classifying disease condition
Zaman et al. [42]	English	1503	BERT Pre-training	Findings	Classifying disease condition
Liu et al.. [43]	English	594	BERT	Not mentioned	Classifying certainty
Proposed study	Chinese	5864	BERT Pre-training DL	Findings	Classifying disease condition