With the world celebrating World Radiography Day on November 8th, 2025, honoring the anniversary of Wilhelm Conrad Röntgen’s discovery of X-Rays in the year 1895, a breakthrough in revolutionising medical diagnosis and treatment, Suraksha Clinic and Diagnostics reaffirms its commitment towards advancing patient care through Artificial Intelligence (AI) driven radiography and precision imaging, marking a new chapter in India’s diagnostic evolution.
In earlier settings before the emergence of AI, radiologists could take a considerable amount of time in analysing complex scans under heavy workloads, with probability of delaying the process of disease detection, treatment planning and recovery. Currently with AI driven imaging systems, radiographic reports are generated and prioritised in real time which allows critical cases to be flagged with immediate effect. Moreover AI tools improve mammography by supporting radiologists, enhancing cancer detection accuracy rates and reducing chances of errors such as false positives, false negatives, further helping to optimise workflow.
Integration of AI has revolutionised Radiography, enhancing the entire diagnostics workflow, starting from image acquisition to interpretation and the subsequent treatment planning. In the present times, advanced digital imaging and powerful AI algorithms are leveraged for improving efficiency, accuracy and patient care.
Speaking on how AI is bringing a transformation in radiography and modern diagnostics, Dr Krishanu Saha, Senior Consultant Radiologist at Suraksha Clinic and Diagnostics said, “AI is transforming the field of radiography from a purely visual, manual interpretation process into a highly-efficient, data-driven discipline, significantly enhancing the capabilities of a physician and improving patient outcomes. AI is no longer just assisting radiologists; it is reshaping the entire imaging ecosystem from faster scan analysis and early disease detection to predictive diagnostics and personalised treatment planning.”
AI is integrated across several imaging modalities, which include X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET).
AI also aids in the creation of personalised treatment plans and in the prediction of patient outcomes by way of analysing huge amounts of imaging data besides genetics and clinical information (radiomics).
However AI systems can sometimes lead to biases and inaccuracies because of inconsistencies in image quality, formats and labeling across hospitals. AI systems can also reflect regional or demographic biases in case the dataset does not represent diversified populations. AI driven diagnostics face stringent regulatory approval processes for ensuring patient safety and data privacy. Several tools are still awaiting validation or clearance in real-world clinical settings. Without adequate training and explainable AI, AI adoption remains slow despite its potential. High initial setup costs, requirement for advanced hardware and maintenance expenses can act as barriers.
The next wave of AI will help in transitioning radiography from diagnostics to predicting disease risks – integration of data from genomics, imaging, and patient history to anticipate illness progression, redefining preventive healthcare and patient management in the growing digital health ecosystem in India.