About Us
The Center for Artificial Intelligence and Data Science engages with other institutes and groups within Cleveland Clinic to support and strengthen activities and initiatives for research, process improvements & clinical care enhancements.
Our major goals include:
- Making Cleveland Clinic the most Artificial Intelligence & Machine Learning (AI/ML) literate medical institution in the world by providing unique educational resources.
- Providing AI/ML support on research, quality and operational initiatives throughout Cleveland Clinic.
- Serving as a major incubator of AI/ML innovations (i.e. novel software and technology platforms).
- Re-imagining how clinicians diagnose and treat all human diseases.
- Incorporating automation and machine learning to optimize operational workflows, reduce cost and improve staff utilization.
- Using AI/ML and data to make RT-PLMI the most efficient and safe environment for laboratory testing in the world.
- Using digital pathology to augment our world-class group of anatomic pathologists to enhance our ability to diagnose tissue-based specimens efficiently, safely, and reproducibly.
Mission
Research & Innovation
- Serve as a major incubator of AI/ML innovations (i.e. novel software and technology platforms).
- Build new ML tools that address current & future needs.
- Provide AI/ML support on research, quality and operational initiatives.
Clinical & Operational Tools
- Employ automation and machine learning to optimize operational workflows, reduce cost and improve staff utilization.
- Utilize AI/ML and data to make RT-PLMI the most efficient and safe environment for laboratory testing in the world.
- Use digital pathology to enhance our ability to diagnose tissue-based specimens efficiently, safely, and reproducibly.
Education
- Re-imagine how we diagnose and treat all human diseases.
- Create new fully Interactive AI/ML in Healthcare e-learning courses.
- Make Cleveland Clinic the most AI/ML-literate medical institution in the world by providing unique educational resources.
Leadership
Samer Albahra, MD
Co-Director, Center for Artificial Intelligence and Data Science
Director, Research and Development, Pathology & Lab Medicine
Scott Robertson, MD, PhD
Co-Director, Center for Artificial Intelligence and Data Science
Director, Image Analytics, Pathology & Lab Medicine
Bo Hu, PhD
Director, Scientific Affairs, Pathology & Lab Medicine
Daniel Lallo
Administrator, Center for Artificial Intelligence and Data Science
Research & Innovation
We work closely with basic science and clinical research groups for their AI/ML needs. In addition, we are building ML tools that address current and future needs. For example, one major need in data science is easier access to data. Regulatory aspects prevent this from happening, but one solution is to use synthetic data that emulates real data. This method is great for pilot studies and encourages staff to do more studies and to test out their ideas with fewer regulatory aspects.
Machine learning tools
We are working on several ML innovation tools that started in 2022 and have been incubating at Cleveland Clinic:
STNG (Synthetic Tabular Neural Generator)
STNG is a novel, powerful automated ML tool for tabular synthetic data generation. It uses a combination of sophisticated platforms to build synthetic (not representative of a real patient) datasets that can emulate their real dataset counterparts. In short, it allows you to start with synthetic data for your pilot ML or Non-ML study to assess feasibility before requesting IRB approval.
PIRO (Pathology Information Retrieval Optimizer)
- Fast, easy data searches.
- Compliance with regulatory stakeholders (IRB, HIPPA, Legal).
- Data extraction workflow.
- Workflow for building cohorts, adding annotations, and generating study barcodes.
Education
AI/ML in Healthcare Course
We've created a new AI/ML in Healthcare e-learning course, which launched in early 2024. This course is fully interactive, healthcare-focused, and no coding or programming is required. Participants are provided tools and sample datasets to participate in and complete interactive projects; create image-based ML models (e.g. a Cancer versus No Cancer prediction tool, etc.); and make tabular data-based ML models (e.g. Diabetes outcome predictor tool, etc.) By the end of the course, participants will gain an enhanced AI/ML literacy and a better understanding of ML-related statistics.
Course instructors
Hooman Rashidi, MD
Course Instructor: ML basics, Statistics Overview, Deep Learning & Supervised Algorithms & Unsupervised Methods
Walter Henricks, III, MD
Course Instructor: Data Acquisition, Visualization & Regulatory Aspects
Scott Robertson, MD, PhD
Course Instructor: Image Analytics/ Deep Learning Course Project
Samer Albahra, MD
Course Instructor: Natural Language Processing & ML Deployment
Bo Hu, PhD
Course Instructor: Regression Modeling and Related Statistics
