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Artificial intelligence (AI) has sparked a medical revolution, addressing crucial challenges like healthcare costs, disease management, accessibility, and treatment enhancement. The doctor-patient interaction has significantly evolved in the modern era thanks to AI. The cost and accessibility of healthcare remain pivotal issues, especially in rural and urban areas. AI harnesses algorithms such as machine learning (ML) and deep learning (DL) to expedite disease diagnosis, facilitating early detection and reducing complications. Clinical decision support systems (CDSs), like diagnostic CDS, aid clinicians by automating rule-based diagnosis. Successful integration of these systems is evidenced in electronic medical records (EMRs). Newer DL techniques, including the utilization of contrast details from non-contrast imaging via a two-dimensional cycle generative adversarial network (cycle GAN), showcase advancements. Further refinement of this model holds promise in mitigating complications associated with contrast agent use, underlining AI's potential for transformative impact in the medical field.
Artificial Intelligence (AI) has initiated a transformative shift in healthcare by effectively addressing vital challenges encompassing healthcare expenditures, disease management, accessibility, and treatment enhancements. In the modern healthcare landscape, AI has substantially reshaped the doctor-patient interaction. Persistent concerns regarding healthcare cost and accessibility, particularly in remote and urban regions, persist.AI leverages sophisticated algorithms, such as Machine Learning (ML) and Deep Learning (DL), to expedite disease diagnosis, facilitating early detection and complications reduction. Clinical Decision Support Systems (CDSs), exemplified by diagnostic CDS, empower clinicians through automated rule-based diagnosis, as corroborated by their integration into Electronic Medical Records (EMRs).
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