This section systematically reviews the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Artificial Intelligence (TRIPOD+AI, 2024) and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Large Language Models (TRIPOD-LLM, 2025) guidelines, which focus on the transparent reporting of clinical Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models. It first outlines how methodological requirements have evolved since the classical version of TRIPOD. In the second part, it presents detailed checklists for data management, model development-validation, interpretability, ethics, and bias assessments, based on the fourteen additional items of TRIPOD+AI and the LLM-specific principles of TRIPOD-LLM. Common pitfalls such as missing data handling, data leakage, reliance on single metrics, and lack of external validation are exemplified, and corresponding prevention strategies are discussed. The final section emphasizes the theoretical guidance role of biostatisticians and outlines a future research agenda in light of federated learning, privacy-preserving AI, and fairness-oriented evaluations.