A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform
Pascal Thériault-Lauzier, Denis Corbin, Olivier Tastet, Élodie Labrecque Langlais, Bahareh Taji, Guson Kang, Aun‐Yeong Chong, Derek So, An Tang, Judy Wawira Gichoya, Sarath Chandar, Pierre-Luc Déziel, Julie Hussin, Samuel Kadoury, Robert Avram
Abstract
The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyze medical images, thereby improving diagnostic precision and accuracy, thus enhancing current tests. However, the integration of AI within healthcare is fraught with difficulties. Heterogeneity among healthcare system applications, reliance on proprietary closed-source software, and rising cyber-security threats pose significant challenges. Moreover, prior to their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow which is difficult to achieve without dedicated software. Finally, the use of AI techniques in healthcare raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device’s safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in healthcare and provides guidelines on how to move forward. We describe an open-source solution that we developed which integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offers a pathway towards responsible, fair, and effective deployment of AI models in healthcare. Additionally, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model, to enhance standardization and reproducibility. The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyze medical images, thereby improving diagnostic precision and accuracy, thus enhancing current tests. However, the integration of AI within healthcare is fraught with difficulties. Heterogeneity among healthcare system applications, reliance on proprietary closed-source software, and rising cyber-security threats pose significant challenges. Moreover, prior to their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow which is difficult to achieve without dedicated software. Finally, the use of AI techniques in healthcare raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device’s safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in healthcare and provides guidelines on how to move forward. We describe an open-source solution that we developed which integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offers a pathway towards responsible, fair, and effective deployment of AI models in healthcare. Additionally, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model, to enhance standardization and reproducibility.