New computer technology called generative AI is making big changes in healthcare. It’s helping doctors work better and making things easier for patients. This smart technology can help find diseases faster, make new medicines, and even control robots for surgery. It looks at lots of information to give each patient the best care possible.

The healthcare sector is transforming with the use of IoT, AI/ML, smart wearables, robotics, and virtual and augmented reality. These technologies improve patient lives by reducing chaos, enabling early disease detection, remote monitoring, improved medication administration, and telemedicine.

The Internet of Medical Things (IoMT) is a growing trend in healthcare, enabling medical devices to gather, transmit and receive data. These devices including patient remote monitoring, and smart wearables are gaining popularity due to chronic disease and the aging population. IoMT devices help in the continuous flow of patient data, which helps the healthcare provider to have real-time access to health information.

Digital transformation in healthcare uses technology to improve patient care, reduce costs, and enhance patient experiences. It improves efficiency, communication, and real-time monitoring of health metrics, leading to faster appointment scheduling, reduced wait times, and a more effective healthcare system.

Extended Reality (XR), comprising Virtual Reality (VR) and Augmented Reality (AR), transforms healthcare by enhancing patient engagement, surgical planning, and medical education. The post-pandemic surge in virtual healthcare adoption amplifies the demand for XR technologies. eInfochips, a leader in AR, VR, and Mixed Reality services, breaks physical boundaries through design, AI, and XR expertise, contributing to a potential $19.6B AR/VR healthcare market by 2030.

This article explores the critical process of image quality tuning in medical imaging, highlighting its significance in accurate diagnoses and groundbreaking research. It delves into the objective and subjective aspects of tuning, emphasizing the delicate balance between technological accuracy and human perception. It addresses common challenges in fine-tuning, such as contrasting metrics and patient variability, and discusses modalities-based adaptation.