Quantum Computing for Enhancing AI Models in Healthcare Diagnostics: a Theoretical Perspective

EasyChair Preprint 15355

Abstract

 Artificial intelligence (AI) has brought
transformative potential to healthcare, with its uses
extending from diagnostics to personalized care.
However, traditional AI models, including deep
learning networks, face significant challenges in
computational demand, data complexity, and pro-
cessing speed. Quantum computing, with its excep-
tional computational power, offers a promising solu-
tion. This paper examines how quantum computing
can enhance AI models in healthcare diagnostics.
Through analyzing algorithms like Quantum Neural
Networks (QNNs) and Quantum Approximate Opti-
mization Algorithm (QAOA), we provide a theoreti-
cal perspective on the potential for improvements
in diagnostic accuracy, efficiency, and scalability.
The paper highlights the constraints of classical
AI models and how quantum technology could
overcome these limitations, providing new directions
for research into quantum-powered AI in healthcare

Keyphrases: Artificial Intelligence, Healthcare Diagnostics, quantum computing

@booklet{EasyChair:15355,
  author    = {Sai Akhil Kona and Sai Kiran Buchiraju},
  title     = {Quantum Computing for Enhancing AI Models in Healthcare Diagnostics: a Theoretical Perspective},
  howpublished = {EasyChair Preprint 15355},
  year      = {EasyChair, 2024}}