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Artificial Intelligence (AI) is reshaping the future of healthcare, with medical imaging at the forefront of this transformation. From detecting abnormalities in X-rays and MRIs to predicting disease progression, AI-driven tools enhance diagnostics' speed, accuracy, and efficiency.
According to the market research firm GlobalData, India’s diagnostic imaging device market is projected to exceed $7 billion by 2033 and grow at a compound annual growth rate (CAGR) of 7 percent.
As of 2024, India accounts for approximately 20%of the Asia-Pacific (APAC) diagnostic imaging market, with portable imaging playing a crucial role in improving accessibility.
Further, as healthcare systems worldwide struggle with increasing patient loads and a shortage of radiologists, AI offers scalable solutions that can support timely and precise decision-making.
India, in particular, stands to benefit immensely from this technological evolution. Industry data suggests that for every 100 diagnostic scans conducted daily, only one radiologist is available to interpret the results. Since the country’s radiologist-to-population ratio is far below global recommendations, AI can help bridge critical gaps in access and efficiency, especially in underserved and remote areas.
This article delves into the intersection of technology and diagnostics, wherein AI is augmenting medical imaging and harnessing its latent potential.
How AI Enhances Medical Imaging
AI is significantly enhancing image processing through:
Facilitates Noise Reduction: AI algorithms are employed to improve the clarity of medical images by effectively reducing noise, which is crucial for accurate diagnoses. A 2024 study in the Indian Journal of Science and Technology presented a deep learning method that uses Convolutional Neural Networks (CNNs) and denoising autoencoders to improve low-light medical images. In simple terms, the CNNs help identify key features in noisy images, while the autoencoders clean up the images by removing the noise and keeping the essential details. This makes the images clearer and more useful for accurate diagnosis.
Enhanced Image Segmentation: AI helps outline body parts and disease-affected areas in medical images, making work easier for doctors and radiologists. At Jio Institute in India, the Computer Vision in Medical Imaging (CVMI) project uses AI to automate tasks like spotting lesions, dividing up image regions, and tracking disease, all designed for the Indian healthcare setting. This makes medical image analysis faster and more accurate.
Better Pattern Recognition: AI excels in identifying complex patterns indicative of specific diseases, thereby enhancing early detection and diagnosis. Indian companies like Qure.ai have developed AI-powered solutions that assist in interpreting medical images, effectively detecting abnormalities, and diagnosing conditions such as tuberculosis and lung diseases.
In 2023, Qure.ai received FDA 510 (k) clearance for its qXR chest X-ray solution. The solution can triage pneumothorax (PTX) and pleural effusion (PE), which pose formidable challenges in emergency medical settings. Such tools support healthcare professionals in making faster and more accurate decisions, particularly in resource-constrained settings.
These advancements in AI-driven image processing contribute to more accurate diagnoses and efficient patient management in India, addressing challenges posed by high patient volumes and the limited availability of specialized radiologists.
Advantages of Using AI in Medical Imaging
AI applications in medical imaging span a wide range of modalities, each with distinct clinical advantages:
X-Ray: AI algorithms assist in the detection of bone fractures, pulmonary infections (e.g., pneumonia, tuberculosis), and lung abnormalities, improving both diagnostic speed and accuracy.
In 2024, the National Institute for Health and Care Excellence (NICE) in England approved AI tools such as TechCare Alert, Rayvolve, BoneView, and RBfracture to assist in detecting bone fractures from X-rays. These tools aim to reduce missed or late diagnoses, where up to 10% of fractures are overlooked in initial assessments.
A study titled "Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs (2019)” published by JAMA (Journal of the American Medical Association) found that AI was more accurate in diagnosing chest X-rays.
The AUC Performance Evaluation revealed that AI had a higher true positive rate and a lower false positive rate than doctors, including thoracic radiologists, radiologists, and physicians. The higher the curve, the more accurate the diagnosis.
AI detected images with higher sensitivity and specificity than doctors, as shown by the ROC curves in the figure above.
Recently, Union Health and Family Welfare Minister JP Nadda announced that the government is integrating AI into healthcare to improve disease detection and treatment. AI-powered X-ray machines are being used to detect tuberculosis (TB) early. At the same time, AI is also being leveraged to diagnose sickle cell disease and enhance cancer treatment, even in rural areas.
Magnetic Resonance Imaging (MRI): AI enhances the identification and characterization of neurological disorders such as Alzheimer’s disease, multiple sclerosis, and musculoskeletal injuries by supporting automated segmentation and pattern recognition.
Furthermore, AI-powered tools can significantly reduce scan times by optimizing imaging protocols, which speeds up diagnosis and improves patient comfort.
In May 2022, Synapsica, an Indian AI radiology reporting company, partnered with Aarthi Scans and Labs, a diagnostic chain operating over 40 centers and conducting over 900 MRI scans daily. Synapsica implemented its AI assistant, 'Spindle,' through this collaboration to automate the preliminary reporting of spine MRIs.
The spindle can automatically identify over 35 degenerative spinal pathologies and provide detailed measurements and standardized reports. This integration has resulted in a 70% reduction in the time radiologists spend on spine MRI evaluations, enhancing efficiency and diagnostic accuracy.
In December 2024, Aarthi Scans & Labs partnered with Annalise.ai, an Australian developer of AI solutions for medical imaging. This collaboration introduced AI technology to prioritize emergency CT brain studies, enabling radiologists to deliver reports within 10 minutes for critical cases like strokes and head injuries. The AI algorithms analyze CT brain scans with high accuracy, swiftly identifying potential abnormalities and highlighting them for radiologists. This advancement accelerates the diagnostic process and enhances reporting accuracy, improving patient outcomes.
Computed Tomography (CT): AI supports detecting tumors, hemorrhages, and vascular diseases rapidly. Its real-time analysis capabilities are instrumental in emergency and oncologic imaging. AI applications in neuroimaging have shown promise in managing increasing imaging volumes and providing timely diagnoses for neurological conditions.
For instance, Qure.ai’s qER solution uses deep learning algorithms to detect critical abnormalities such as intracranial hemorrhages, midline shifts, and skull fractures in head CT scans. This tool is particularly valuable in emergencies, helping clinicians prioritize urgent cases and make faster, more accurate decisions.
According to a 2024 study published in the European Radiology Experimental, AI models have demonstrated a 76% agreement with radiologists in quantifying vascular involvement from CT images. This shows that AI has a strong potential to help evaluate vascular conditions accurately. Hence, by complementing radiologists in decision-making, AI helps reduce human errors, particularly those arising from fatigue or oversight.
Ultrasound: AI improves the precision of fetal growth assessments, cardiac evaluations, and abdominal organ analysis by minimizing user variability and automating key measurements.
According to the findings of a study, “Application and Progress of Artificial Intelligence in Fetal Ultrasound (2023)”, published in the Journal of Clinical Medicine, AI-based models have achieved an accuracy of 83.8% in predicting gestational age from ultrasound images, showing good stability and reproducibility. This advancement holds the potential for assessing neonatal respiratory distress syndrome.
A notable example is Wipro GE Healthcare's launch of the Versana Premier R3 in March 2025. This AI-enabled ultrasound system is designed to enhance clinical efficiency, streamline workflows, and improve diagnostic accuracy. Manufactured at Wipro GE Healthcare's PLI factory in Bengaluru, the Versana Premier R3 aligns with the 'Make in India' initiative, emphasizing the country's commitment to developing advanced medical technologies domestically.
Positron Emission Tomography (PET): AI enhances image quality, quantification, and lesion detection to contribute to early cancer detection and monitoring of metabolic disorders.
In India, the All India Institute of Medical Sciences (AIIMS) in New Delhi, in collaboration with the Centre for Development of Advanced Computing (CDAC), Pune, developed an AI-based platform called iOncology.ai. This platform assists in the early detection of breast and ovarian cancers by analyzing a vast dataset of approximately 500,000 radiological and histopathological images from around 1,500 patient cases. During testing, iOncology.ai achieved prediction scores above 75% in identifying breast and ovarian tumors, demonstrating its potential utility in assessing patient risk, predicting treatment responses, and estimating survival outcomes. The platform has been implemented in five district hospitals nationwide to aid clinicians in making more informed decisions regarding cancer diagnosis and treatment.
Therefore, in resource-limited settings, AI offers scalable solutions that can extend the reach of quality diagnostic services while addressing challenges related to the shortage of skilled radiologists.
Challenges
Despite their advantages, AI systems face several critical challenges that must be addressed to ensure effective and ethical deployment:
Data Privacy & Security Concerns: The protection of sensitive patient data is paramount. India's healthcare sector has witnessed significant cyberattacks, with over 1.9 million incidents reported in 2022 alone, including a notable breach at the All India Institute of Medical Sciences (AIIMS) in New Delhi.
Such incidents underscore the urgent need for robust data protection measures to prevent unauthorized access and ensure patient confidentiality. The Implementation of the Digital Personal Data Protection Act (DPDPA) in 2023 aims to provide a regulatory framework for digital data security. Still, its implications for medical personnel remain a topic of ongoing discussion.
Bias in AI Models: AI systems can inadvertently perpetuate biases in training data, leading to disparities in healthcare outcomes. For instance, if an AI algorithm is trained predominantly on data from urban populations, its applicability to rural communities may be compromised, resulting in inaccurate predictions or diagnoses. Addressing these biases requires the development of diverse and representative datasets and implementation of fairness measures during model training.
Interpretability & Trust in AI Decisions: The "black box" nature of some AI models can hinder their acceptance in clinical settings. Healthcare professionals may be reluctant to rely on AI-driven recommendations without clear insights into decision-making. Developing explainable AI (XAI) systems is essential for building trust among clinicians and patients, ensuring that AI decisions are transparent and can be effectively integrated into medical practice.
Integration with Existing Healthcare Systems: Integrating AI solutions with India's healthcare infrastructure poses interoperability and adoption challenges. Many healthcare institutions utilize legacy systems that may be incompatible with new AI technologies. Ensuring seamless integration requires collaboration between technology developers and healthcare providers to address data standardization and system compatibility issues.
Future Trends & Innovations
AI is rapidly transforming medical imaging in India, driving innovations that enhance diagnostic accuracy, streamline clinical workflows, and expand access to quality care. As integration with telemedicine grows and regulatory frameworks evolve, AI is poised to play a pivotal role in making medical imaging more efficient, accessible, and impactful across the country. A few of these emerging trends are:
Federated Learning (FL): In India, protecting patient data is a primary concern in healthcare, primarily as more hospitals and clinics use digital systems. One promising solution is Federated Learning (FL), a type of AI that allows hospitals to work together to train models without sharing patient data. Instead, the AI learns from data stored locally at each site, helping improve diagnostic accuracy while keeping personal health information private. Such innovations are expected to effectively address the privacy concerns related to AI in medical imaging.
AI in Large-Scale Health Screening: AI plays a crucial role in large-scale health screenings by efficiently analyzing medical images to detect diseases early. This is particularly significant in India, where the vast population presents challenges for timely diagnostics.
AI-powered medical imaging can facilitate earlier detection and treatment, ultimately improving population health outcomes. For example, a Bangalore-based startup, Niramai, developed Thermalytix, an AI-driven thermal imaging solution for non-invasive breast cancer detection. This method is affordable and accessible, making it particularly suitable for rural areas where traditional mammography facilities are scarce.
Also, recently, Molbio Diagnostics collaborated with Niramai Health Analytix to accelerate the adoption of Thermalytix in developing countries worldwide. The partnership aims to enable efficient screening of large populations, especially in low-resource settings, a critical healthcare need across many geographies today.
At Maha Kumbh Mela 2025, one of the world’s largest religious gatherings, Qure.ai deployed its AI-enabled chest X-ray interpretation tool, qXR, to support medical efforts at the event. The AI solution was operational at the Central Hospital in Sector 2, the main medical facility dedicated to handling the immediate healthcare needs of the pilgrims. Designed for quick triaging and TB surveillance, qXR analyzed chest X-rays in real-time, automatically flagging abnormalities and identifying potential TB cases, even when the X-ray was conducted for unrelated reasons.
Personalized Medicine through AI: AI's application in medical imaging advances personalized treatment plans by providing detailed insights into individual patient conditions. By analyzing imaging data alongside other health information, AI enables tailored therapeutic strategies, enhancing the effectiveness of treatments and patient care.
Last year, Apollo Cancer Centre unveiled the AI-Precision Oncology Centre (POC) in Bengaluru, setting a new standard in cancer care. This innovative center aims to harness the power of artificial intelligence (AI) to provide swift and accurate treatment options for oncologists, patients, and caregivers.
Designed to be patient-centric, it identifies eligible patients for targeted therapy and immunotherapy during diagnosis and treatment planning. Utilizing conversational AI, the center educates patients and their families on diagnosis and treatment FAQs and connects them with support groups.
Multimodal AI Approaches: Integrating imaging data with genomics and clinical information can lead to more accurate predictions and personalized treatment plans, advancing precision medicine. In India, initiatives are underway to harness AI to analyze diverse data types.
In January 2025, India launched the Indian Genomic Data Set and the Indian Biological Data Centre (IBDC) portals, making 10,000 whole genome samples available to researchers globally. This initiative aims to build a self-reliant biotech ecosystem, facilitating the integration of genomic data with clinical and imaging information for personalized healthcare solutions.
Recently, Apollo Hospitals inaugurated the Apollo Genomics Institute in Karnataka, a state-of-the-art facility that integrates genetic diagnostics into routine healthcare. This initiative significantly advances personalized medicine in India, enhancing early disease detection, treatment precision, and risk assessment.
Edge AI in Medical Imaging: Going forward, deploying AI algorithms directly on imaging devices is expected to enable real-time diagnostics, reducing the need for data transmission and allowing for immediate clinical decisions.
The Medical Imaging Datasets for India (MIDAS) platform is a recently launched initiative to build a repository of quality-graded health data to support AI-driven healthcare in the country. Developed through a collaboration between the Indian Council of Medical Research (ICMR), the Indian Institute of Science (IISc), and ARTPARK, MIDAS is designed to host high-quality, standardized medical datasets that accurately reflect the Indian population. The platform operates on a technology-enabled hubs-and-spokes model, where central hub institutions collaborate with multiple spoke institutions to collect and curate health and medical data focused on specific diseases.
AI and Augmented Radiology: The role of radiologists is evolving to include collaboration with AI systems, leveraging AI's analytical capabilities while applying human expertise to complex cases.
A study titled “Collaboration between Clinicians and Vision–Language Models in Radiology Report Generation (2024)),” published in Nature, introduced Flamingo-CXR, an AI system designed to generate radiology reports for chest radiographs. Evaluations by certified radiologists in the United States and India revealed that in over 60% of cases, AI-generated reports were preferred or considered equivalent to human-written reports. This suggests that AI can effectively assist in drafting reports, allowing radiologists to review and finalize them efficiently.
Recently, University Hospitals Cleveland Medical Center (UH) announced a groundbreaking collaboration with global healthcare Artificial Intelligence (AI) innovator Qure.ai to deploy chest X-ray AI, supporting earlier identification of lung cancers. The FDA-cleared chest X-ray AI solution qXR-LN will act as a second read to be compared to the radiologists' read of patient chest X-rays for any suspicious lung nodules. This will also provide evidence for future AI research.
Chatbots in Medical Imaging: AI chatbots have gained significant traction in medical imaging as tools like ChatGPT and Grok.aiare increasingly being used by healthcare professionals and even patients to decode and understand X-ray and scan reports. These conversational AI tools assist radiologists by quickly summarizing imaging findings, flagging potential anomalies, offering differential diagnoses, accelerating decision-making, and reducing reporting time.
While these tools are not replacements for expert interpretation, they are valuable in enhancing efficiency, supporting clinical decisions, and improving patient communication. As AI tools become more sophisticated, their integration into healthcare could significantly improve diagnostic accuracy and patient outcomes.
A Promising Future Ahead
Integrating AI into medical imaging holds immense potential for transforming diagnostics and patient care. While challenges remain, addressing those is crucial for AI's successful and ethical implementation in India's healthcare sector. Therefore, by embracing AI responsibly, the healthcare industry can enhance diagnostic accuracy, efficiency, and accessibility, ultimately improving patient outcomes.
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