Table 2.

AI-specific reporting and risk-of-bias frameworks relevant to AI research in esophageal cancer.

Standard	Full Name/Focus	Year	Checklist structure	Primary Application
TRIPOD+AI (71)	Updated reporting guidance for clinical prediction models using regression or machine learning	2024	27 items, including subitems	Reporting of AI/ML prediction model studies (development and validation).
PROBAST+AI (72)	PROBAST+AI: quality, risk-of-bias, and applicability assessment for prediction models using regression or AI	2025	Separate development and evaluation assessments across four domains	Risk-of-bias assessment of AI/ML prediction model studies.
CLAIM (74)	Checklist for Artificial Intelligence in Medical Imaging	2024 update	44 items	Reporting of medical imaging–based AI studies, including radiomics.
MI-CLAIM (75)	Minimum Information about Clinical Artificial Intelligence Modelling	2020	6-part checklist	Reporting of broader clinical AI studies, including non-imaging data.
STARD-AI (73)	Standards for Reporting Diagnostic Accuracy Studies - AI	2025	40 items	Diagnostic-accuracy studies of AI-based tests.
SPIRIT-AI (76)	SPIRIT extension for clinical trial protocols involving AI	2020	15 extension items	Trial protocols for AI interventions.
CONSORT-AI (77)	CONSORT extension for clinical trial reports involving AI	2020	14 extension items	Completed RCTs of AI interventions.
DECIDE-AI (78)	Developmental and Exploratory Clinical Investigation of DEcision support systems driven by AI	2022	27 items	Early-stage clinical evaluation of AI-based decision-support systems.

AI, artificial intelligence; ML, machine learning; RCT, randomized controlled trial. Versions and structures refer to the cited publications. Reporting completeness, risk of bias, and clinical readiness are separate assessments.