Quantum-enhanced medical imaging: precision advancements in diagnostic accuracy

Seyed‐Sajad Ahmadpour, Danial Bakhshayeshi Avval, Mehdi Darbandi +3 more

Anurag Shrivastava, Ramy Riad Al–Fatlawy, Ginni Nijhawan +3 more

Quantum computing is fast transforming the medical world and giving it a new level of speed, accuracy, and safety in the health-care information. The latest developments use quantum machine learning and quantum-classical to improve medical imaging like classification, segmentation, and disease prediction, going beyond the capability of the classical algorithms. In the multifactorial disease cases, like cancer and neurological diseases, quantum-enhanced analytics will yield better clinical prognoses, better biomarker discoveries, and earlier detection. In the meantime, quantum-cryptography protocols are under development that will provide protection against privacy threats that are constantly changing to harm patient data. State-of-the-art quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) continue to optimize the process of diagnostics, reducing resource consumption, without losing the level of accuracy. This review maps these new applications, identifies the new trends, and talks about the future challenges of such applications, especially the requirement of explainable, interpretable quantum AI, which the clinicians can believe in. In brief, quantum technologies are in their infancy, but will have the ability to advance precision medicine, customised treatment and revolutionise medical decision-making.

Kriti Sharma, Adarsh Kumar

ZX-calculus is an important quantum calculi and optimization approach for quantum circuit simplification and enhancing the efficiency and accuracy of medical data processing in healthcare systems. In this work, an optimization and reduction of quantum circuit complexity is performed for handling vast and intricate healthcare data. The quantum computing-based QAOA is applied to improve performance in tasks such as medical imaging analysis, genomic data processing and predictive modeling. Observations are associated with precise diagnostics and personalized treatment plans. As a result, computational efficiency has been observed to improve while ensuring the security and efficiency of sensitive medical information. This ultimately supports in advancement of the healthcare domain and associated data analysis through quantum computing.

Hugo Defienne, Warwick P. Bowen, Maria V. Chekhova +5 more

Wolley, Osian

Md Majedul Islam, Jing Selena He

Machine learning methods have made huge improvements to medical imaging, particularly in the utilization of CT scans for intricate trauma cases. This paper initiates an exploratory journey, employing a hybrid quantum machine learning (HQML) approach to enhance the detection accuracy of abdominal trauma. Our study meticulously curates an extensive dataset from abdominal CT scans, navigating the inherent challenges presented by the voluminous nature and complex features of such medical data. We use quantum transfer learning techniques in a new way by combining the complex details of these scans into a computing framework that combines the powerful pattern recognition of quantum computing with the stability of classical machine learning. Our approach positions itself at the lead of medical innovation, poised to refine diagnostic precision and accelerate therapeutic protocols through an advanced analytical perspective. By looking at how well classical ResNet architectures and quantum models work, we find small differences in how well they work. This shows that quantum algorithms could decode medical images with a level of good accuracy. Our findings do not merely highlight the transformative potential of quantum computing in medical diagnostics but also pave the way for ensuing explorations in the domain of hybrid quantum-classical machine learning solutions.