Advancing Cancer Diagnostics and Patient Care: Harnessing Safe AI for Accurate Multimodal Healthcare Solutions
Disciplines
Artificial Intelligence and Robotics | Bioimaging and Biomedical Optics | Cell Biology | Computer Sciences | Pathology
Abstract (300 words maximum)
With the rapid evolution of Artificial Intelligence (AI) models like ChatGPT-4, healthcare is witnessing a transformative shift through diverse applications that enhance patient care, diagnostics, and operational efficiency. These models can support various healthcare needs. For instance, AI systems can assist in patient triage by analyzing symptoms and offering preliminary advice when immediate medical staff are unavailable. In diagnostics, AI can be trained to recognize patterns in cancer cells, identifying irregularities like deformed cell membranes or nuclei, thus improving diagnostic speed and accuracy. They also streamline medical documentation, summarizing and organizing information from clinical notes to save physicians time and ensure accessibility to crucial data. In risk assessment, AI analyzes patient histories and current data to help predict potential health issues, supporting preventative care. Additionally, patient engagement tools powered by AI improve interaction via virtual assistants that answer health questions, schedule appointments, and assist with medication reminders. AI also enhances remote monitoring, processing data from wearables or home-monitoring devices to alert providers to real-time anomalies. As AI models become more sophisticated, their applications continue to expand, offering solutions for improved healthcare access, diagnostic accuracy, and operational support. This research focuses on leveraging multimodal AI capabilities to support cancer diagnosis and patient interaction, specifically developing an AI assistant capable of accurately interpreting microscopy images of cancer cells. However, significant discrepancies in safe AI remain, as these systems are still prone to inaccuracies, occasionally generating incorrect and fake information. This underscores the critical importance of AI safety and the need for stringent development practices to ensure AI systems provide reliable, fact-based support without introducing false data.
Academic department under which the project should be listed
SPCEET - Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Razvan Voicu
Advancing Cancer Diagnostics and Patient Care: Harnessing Safe AI for Accurate Multimodal Healthcare Solutions
With the rapid evolution of Artificial Intelligence (AI) models like ChatGPT-4, healthcare is witnessing a transformative shift through diverse applications that enhance patient care, diagnostics, and operational efficiency. These models can support various healthcare needs. For instance, AI systems can assist in patient triage by analyzing symptoms and offering preliminary advice when immediate medical staff are unavailable. In diagnostics, AI can be trained to recognize patterns in cancer cells, identifying irregularities like deformed cell membranes or nuclei, thus improving diagnostic speed and accuracy. They also streamline medical documentation, summarizing and organizing information from clinical notes to save physicians time and ensure accessibility to crucial data. In risk assessment, AI analyzes patient histories and current data to help predict potential health issues, supporting preventative care. Additionally, patient engagement tools powered by AI improve interaction via virtual assistants that answer health questions, schedule appointments, and assist with medication reminders. AI also enhances remote monitoring, processing data from wearables or home-monitoring devices to alert providers to real-time anomalies. As AI models become more sophisticated, their applications continue to expand, offering solutions for improved healthcare access, diagnostic accuracy, and operational support. This research focuses on leveraging multimodal AI capabilities to support cancer diagnosis and patient interaction, specifically developing an AI assistant capable of accurately interpreting microscopy images of cancer cells. However, significant discrepancies in safe AI remain, as these systems are still prone to inaccuracies, occasionally generating incorrect and fake information. This underscores the critical importance of AI safety and the need for stringent development practices to ensure AI systems provide reliable, fact-based support without introducing false data.