The article discusses the importance of transparency in artificial intelligence/machine learning (AI/ML)-enabled medical devices. These devices offer new opportunities in healthcare, including earlier disease detection, improved diagnostics, and personalized treatment. However, they require careful consideration during development and use, including issues such as usability, equity of access, performance bias, and accountability. The U.S. Food and Drug Administration (FDA) has recognized these challenges and released an action plan to promote transparency in AI/ML devices. In 2021, the FDA hosted a virtual workshop to explore ways to enhance transparency for users of AI/ML devices, including information-sharing mechanisms and ways to improve device safety and effectiveness. Workshop participants emphasized that transparency is crucial for ensuring the proper use of AI/ML devices, allowing stakeholders to understand the device's role in clinical workflows and make informed decisions. Transparency also plays a key role in advancing health equity, as it helps identify and manage bias that may impact patient care. For example, a device trained on older adults with diabetes may not work as well for pediatric patients with the same condition. Transparency can also foster trust and confidence in the performance of AI/ML devices. The article highlights the perspectives of various stakeholders, including patients, healthcare providers, payors, and industry members. Patients expressed concerns about the impact of AI/ML devices on their health and the need for clear information about device performance and data security. Healthcare providers emphasized the need for transparency in device information and the importance of reliable mechanisms for reporting device malfunctions. Payors discussed the need for transparency in device performance and the potential impact of continuous learning algorithms on coverage. Industry members suggested a risk-based approach to transparency to maintain a regulatory framework that is least burdensome while mitigating potential proprietary risks. The article concludes that promoting transparency in AI/ML devices is essential for their safe and effective use. The FDA is working to provide information on device safety and effectiveness through various channels, including online databases and guidance documents. However, workshop participants suggested that the delivery of this information may not be sufficient to enhance stakeholder knowledge or their ability to make informed decisions. The article also emphasizes the importance of using appropriate language and delivery methods to accommodate different literacy levels and learning styles. Overall, the article underscores the need for a human-centered approach to transparency in AI/ML devices to ensure their safe and effective use.The article discusses the importance of transparency in artificial intelligence/machine learning (AI/ML)-enabled medical devices. These devices offer new opportunities in healthcare, including earlier disease detection, improved diagnostics, and personalized treatment. However, they require careful consideration during development and use, including issues such as usability, equity of access, performance bias, and accountability. The U.S. Food and Drug Administration (FDA) has recognized these challenges and released an action plan to promote transparency in AI/ML devices. In 2021, the FDA hosted a virtual workshop to explore ways to enhance transparency for users of AI/ML devices, including information-sharing mechanisms and ways to improve device safety and effectiveness. Workshop participants emphasized that transparency is crucial for ensuring the proper use of AI/ML devices, allowing stakeholders to understand the device's role in clinical workflows and make informed decisions. Transparency also plays a key role in advancing health equity, as it helps identify and manage bias that may impact patient care. For example, a device trained on older adults with diabetes may not work as well for pediatric patients with the same condition. Transparency can also foster trust and confidence in the performance of AI/ML devices. The article highlights the perspectives of various stakeholders, including patients, healthcare providers, payors, and industry members. Patients expressed concerns about the impact of AI/ML devices on their health and the need for clear information about device performance and data security. Healthcare providers emphasized the need for transparency in device information and the importance of reliable mechanisms for reporting device malfunctions. Payors discussed the need for transparency in device performance and the potential impact of continuous learning algorithms on coverage. Industry members suggested a risk-based approach to transparency to maintain a regulatory framework that is least burdensome while mitigating potential proprietary risks. The article concludes that promoting transparency in AI/ML devices is essential for their safe and effective use. The FDA is working to provide information on device safety and effectiveness through various channels, including online databases and guidance documents. However, workshop participants suggested that the delivery of this information may not be sufficient to enhance stakeholder knowledge or their ability to make informed decisions. The article also emphasizes the importance of using appropriate language and delivery methods to accommodate different literacy levels and learning styles. Overall, the article underscores the need for a human-centered approach to transparency in AI/ML devices to ensure their safe and effective use.