Quantum Thought is partnering with Stanford Precision Health to revolutionize the field of medical image analysis.
Currently, when a radiologist wants to image a body part, he or she needs to take a series of image slices of the body part, then inspect each image him/herself to identify an anomaly. Though experienced radiologists can do this surprisingly quickly, they consider it to be an extremely tedious process that should be more automated to speed up diagnosis and reduce or eliminate human error.
With Stanford Precision Health, Quantum Thought is developing a quantum and AI-inspired hybrid algorithm to speed up the aforementioned process. The algorithm will synthesize existing data to identify a 3D image of a normative body part, and will flag and categorize irregularities when processing patient images.
The classical version of this algorithm would be extremely computationally intensive, considering the volume of data needed to construct and store data of a full healthy human body. This is where Quantum Thought utilizes the power of quantum algorithms to simplify and reduce the computation time. Additionally, Quantum Thought has exclusive access to a 70-qubit simulator, which can efficiently run this hybrid algorithm.
The team for the quantum imaging project is comprised of the Deputy Director of Precision Health, an expert radiologist at Stanford, and development engineers with a wealth of experience in quantum programming, AI, machine learning, and general algorithm development. Quantum Thought is excited to lead the initiative of automating medical image analysis for doctors everywhere.