ChemicalQDeviceNext Steps in Quantum Computing and Neuroradiology
Dear Healthcare Colleague, my name is Kevin Kawchak, and I am the Founder CEO of ChemicalQDevice. Today is April 13th, 2023 for "Next Steps in Quantum Computing and Neuroradiology"
Key areas of development are required for quantum computers, software, and algorithms to achieve broad adoption in U.S. Hospitals. The opportunity will likely exist to replace substandard legacy methods with more convenient, higher performance hybrid quantum systems. To process brain images, the Healthcare system will likely desire convincing time and cost efficiency measurements in regards to quantum algorithms. [1-7]
Clinical, Financial, and Administrative systems will all likely be involved with the approval process with the emerging technology. Algorithms that run on quantum simulators, may see fastest adoption, as classical computers can be used to compute quantum circuits, with limitations.
As real quantum hardware emerges, new options will likely be presented for improved multimodal imaging and/or explainable AI. In addition, quantum could assist Neuroradiology tasks such as classifications and predictions which could not easily finish running before aiding the patient, would be considered economically Infeasible, or would be thought as impossible due to the datasize and other factors.
In order to implement quantum algorithms running on either simulators or real hardware, their applications must be appropriately Billable, and be ISO/IEC approved. Once these milestones are achieved: quantum based methods will likely be viable to present probable benefit over probable risk in FDA Brain image reconstruction and segmentation submissions. [8-10]
References are available in the comments below. Have a productive rest of your day. Smile.
4/13/23 "Next Steps in Quantum Computing and Neuroradiology"
References: [1] https://news.fnal.gov/2022/07/nyu-langone-plans-to-partner-with-fermilabs-sqms-to-advance-mri-analysis/[2] https://www.ibm.com/downloads/cas/8QDGKDZJ[3] https://www.quantum.gov/wp-content/uploads/2022/03/BringingQuantumSensorstoFruition.pdf[4] https://scholar.harvard.edu/alexeibyl/molecular-qubit-control-and-single-molecule-mri[5] https://pme.uchicago.edu/news/building-better-quantum-sensors[6] https://www.techrxiv.org/articles/preprint/Quantum_Computing_for_Healthcare_A_Review/17198702[7] https://newsroom.ibm.com/2023-03-20-Cleveland-Clinic-and-IBM-Unveil-First-Quantum-Computer-Dedicated-to-Healthcare-Research[8] https://www.iso.org/obp/ui/#iso:std:iso-iec:4879:dis:ed-1:v1:en[9] https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device[10] https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaHealthcare Innovation
Key areas of development are required for quantum computers, software, and algorithms to achieve broad adoption in U.S. Hospitals. The opportunity will likely exist to replace substandard legacy methods with more convenient, higher performance hybrid quantum systems. To process brain images, the Healthcare system will likely desire convincing time and cost efficiency measurements in regards to quantum algorithms. [1-7]
Clinical, Financial, and Administrative systems will all likely be involved with the approval process with the emerging technology. Algorithms that run on quantum simulators, may see fastest adoption, as classical computers can be used to compute quantum circuits, with limitations.
As real quantum hardware emerges, new options will likely be presented for improved multimodal imaging and/or explainable AI. In addition, quantum could assist Neuroradiology tasks such as classifications and predictions which could not easily finish running before aiding the patient, would be considered economically Infeasible, or would be thought as impossible due to the datasize and other factors.
In order to implement quantum algorithms running on either simulators or real hardware, their applications must be appropriately Billable, and be ISO/IEC approved. Once these milestones are achieved: quantum based methods will likely be viable to present probable benefit over probable risk in FDA Brain image reconstruction and segmentation submissions. [8-10]
References are available in the comments below. Have a productive rest of your day. Smile.
4/13/23 "Next Steps in Quantum Computing and Neuroradiology"
References: [1] https://news.fnal.gov/2022/07/nyu-langone-plans-to-partner-with-fermilabs-sqms-to-advance-mri-analysis/[2] https://www.ibm.com/downloads/cas/8QDGKDZJ[3] https://www.quantum.gov/wp-content/uploads/2022/03/BringingQuantumSensorstoFruition.pdf[4] https://scholar.harvard.edu/alexeibyl/molecular-qubit-control-and-single-molecule-mri[5] https://pme.uchicago.edu/news/building-better-quantum-sensors[6] https://www.techrxiv.org/articles/preprint/Quantum_Computing_for_Healthcare_A_Review/17198702[7] https://newsroom.ibm.com/2023-03-20-Cleveland-Clinic-and-IBM-Unveil-First-Quantum-Computer-Dedicated-to-Healthcare-Research[8] https://www.iso.org/obp/ui/#iso:std:iso-iec:4879:dis:ed-1:v1:en[9] https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device[10] https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaHealthcare Innovation