{"id":"c07646cb-a6c4-4c36-bc9b-d9e934c47823","slug":"toward-transparency-implications-and-future-directions-of-artificial-intelligence-prediction-model-reporting-in-healthcare","title":"Toward transparency: Implications and future directions of artificial intelligence prediction model reporting in healthcare","authors":["Ryan Turlip","Jonathan H. Sussman","Jaskeerat Gujral","Felix C. Oettl","Irina-Mihaela Matache","Bhargavi R. Budihal","Ali K. Ozturk","Jang W. Yoon","William C. Welch","Mert Marcel Dagli"],"abstract":"Background: The integration of artificial intelligence (AI) in healthcare is transforming decision-making and analytics to improve patient outcomes. However, the rapid adoption of AI prediction models has outpaced the development of clinical and research guidelines, raising concerns about reliability, validity, and potential biases. Addressing these challenges is essential for the effective integration of AI tools into clinical practice. Summary: AI prediction models present unique challenges, including opaqueness and the need for robust validation frameworks. The TRIPOD+AI guidelines have been introduced to enhance the transparency and rigor of AI prediction model reporting in healthcare. These guidelines aim to standardize practices, ensuring that AI models are reliable and applicable across diverse healthcare settings. Conclusion: The TRIPOD+AI guidelines represent a significant advancement in the standardization of AI prediction models, promoting transparency and methodological rigor. Their adoption is crucial for improving the reliability and reproducibility of AI tools in healthcare, ultimately enhancing patient outcomes. Continuous updates to these guidelines will be necessary to keep pace with rapid advancements in AI technologies. Keywords: artificial intelligence, machine learning, healthcare, prediction models, TRIPOD, transparency, guidelines, clinical applicability","thumbnailUrl":"https://sni-digital-videos.s3.amazonaws.com/placeholders/specialty/computational.png","publishDate":"2025-04-11T00:00:00.000Z","doi":"10.25259/SNI_178_2025","categories":["Computational","Guest Editorial"],"fullTextUrl":"https://surgicalneurologyint.com/wp-content/uploads/2025/04/13492/SNI-16-135.pdf"}