Lead investigator and Director: Reza Forghani, MD, PhD
Senior Investigator and Co-Director: Caroline Reinhold, MD, MSc
The AIPHL lab is involved in the inception, development, and validation of clinical artificial intelligence (AI) applications in partnership with other academic groups and industry. A major area of clinical translation includes oncology, and other major types of diseases, through precision diagnostics and therapeutics. Advancing biomedical imaging technologies and clinical applications, molecular genomic, proteomic, and other clinical information are used for precision health and biomarker development.
Application of Computerized Image Analysis and Machine Learning for Extraction and Integration of Image-Based Features with Other Clinical and Omics Data for Precision Personalized Healthcare
Our lab has an active radiomics program using and/or combining hand-crafted or deep extracted features in oncology in order to enhance diagnostics and enable more precise personalized therapy.
We are also actively involved in application of different machine-learning approaches for construction of prediction models using molecular data. Our vision is to extract and integrate different data types such as image extracted features, molecular features, and other clinical information. Our long-term goal is to leverage and harness the power of informatics and artificial intelligence and apply it to develop smart patient care pathways.
Systems Machine Learning and Advanced Intelligence Research and Clinical Translational (SMART) Imaging Initiative
Our vision, evolving over the past 20 years, has been to create smart imaging systems that automate the operator-machine and patient-machine interfaces, eliminate imaging artifacts and errors, improve disease detection, provide quantitative measures of the disease, and guide therapeutics.
Our Mission is to improve health outcomes through use of AI to improve imaging machine intelligence and clinical performance.
Our goals and objectives are to transform performance of imaging systems throughout the region by introducing machine and AI technology solutions that create standardized high level of utilization and performance of our imaging systems, leading to new screening, diagnostic and therapeutic application of imaging technologies.
We will achieve this through three areas of focus:
Application of Artificial Intelligence Solutions to Address Practical Challenges in the Healthcare Enterprise and Processes
The use of AI to devise practical solutions in healthcare is a priority and an area for which we are optimally positioned. We are interested and actively involved in developing, deploying, or testing solutions that can address immediate healthcare needs and challenges that could have impact for our patient population in the short to intermediate term through streamlining of healthcare processes.
|Targeted Projects||Broad impact domains|
|Improve diagnostic accuracy and precision||Decrease diagnostics operational costs by machine/facility/nationally|
|Reduce operator dependency||Improve patient outcomes|
|Device automation and patient scan customization through adaptive systems||Decrease variability of technology performance and decrease human errors|
|Reduced artefacts and improved image quality||Increase industry partnerships|
|More advanced signal processing to increase the amount of diagnostic information and yield||Create IP and entrepreneurial opportunities|
|Image processing, diagnostic intelligence and report synthesis||Create sponsored projects opportunities, including AIP NIH|
|Outcomes predictive analytics useful for clinical decision support and population health applications||Core support cross-cutting existing research projects/initiatives|