AI in Radiology: Amit Gandhi on Teleradiology’s Role and the Future of Personalised Healthcare
By Arunima Rajan
In an interview with Arunima Rajan, Amit Gandhi, founder and CEO of The Insight Tribe, explores the sweeping changes AI could bring to radiology in the next decade. He reflects on how teleradiology is narrowing critical healthcare gaps and explains the role of advanced imaging techniques in identifying diseases earlier and tailoring treatments to individual patients, offering a glimpse into the future of personalised medicine.
How do you see AI transforming radiology in the next 5-10 years?
AI is set to fundamentally reshape radiology over the next decade by addressing key bottlenecks and enhancing the radiology workflow at every stage—from image acquisition to diagnosis. One of the most significant transformations will come through AI's ability to deliver faster, higher-quality imaging while simplifying radiologists’ workloads.
For example, AI-powered solutions are already improving image acquisition by reducing motion artifacts caused by patient movement, enhancing image quality, and ensuring a smoother patient experience. These advancements will continue to evolve, enabling faster diagnostics with greater accuracy.
AI takes over repetitive tasks such as segmentation, classification and anomaly detection. This automation allows radiologists to concentrate on more complex cases where human expertise is essential. Healthcare systems deal with imaging efficiently and radiologists experience less burnout with AI's help.
In the next 5-10 years, AI will not only act as a support system for radiologists but will also enable healthcare systems to extend the reach of radiology services to underserved regions, bridging the accessibility gap. By complementing radiologists’ expertise, AI will ensure faster, more accurate diagnoses and improve patient outcomes globally.
Are there specific use cases where AI has significantly improved diagnostic accuracy or efficiency?
AI has completely transformed radiology. Workflows and diagnostic accuracy are improving significantly due to AI. For example, AI algorithms can enhance an image's quality by reducing motion artifacts and optimizing resolution, which provides clearer images for accurate diagnoses while ensuring a better patient experience.
Additionally, AI-powered tools automate tasks like segmentation, anomaly detection, and case prioritization. Automated segmentation saves radiologists valuable time by delineating regions of interest, critical for tissue characterization and surgical planning. Lesion detection algorithms assist in pinpointing anomalies with remarkable precision, expediting the diagnosis of conditions like cancer and cardiovascular diseases.
By automating repetitive tasks and prioritizing critical cases, AI enables radiologists to focus on complex challenges, ensuring faster, more accurate diagnoses and ultimately improving patient outcomes.
Amit Gandhi, Founder and CEO, The Insight Tribe
How has teleradiology helped bridge the gap between urban and rural healthcare?
Teleradiology has become a powerful tool to link healthcare between urban and rural areas. Radiologists can interpret medical images remotely and can provide their knowledge to hospitals in remote areas that lack radiologists. This innovation lets patients in underserved areas receive expert diagnostic help without needing to travel long distances.
The adoption of cloud-based platforms and advanced communication technologies helps radiologists view and report scans anywhere and anytime. This greatly improves accessibility. India faces a radiologist shortage, around 15,000 radiologists serve a population of 1.4 billion. This is where teleradiology plays a critical role in addressing this gap.
Teleradiology improves access by connecting rural patients with expert radiologists, ensures faster turnaround times with real-time image reviews for quicker diagnoses, and reduces costs by enabling remote reporting and minimizing the need for onsite radiologists. As technology evolves, it will continue to enhance healthcare delivery and operational efficiency globally.
What are the key challenges in implementing teleradiology on a large scale?
While teleradiology holds immense promise, its implementation on a large scale comes with significant challenges.
The biggest challenge is maintaining the quality of reporting as it scales. Most of the current teleradiology companies struggle with their delivery model when their business scales. Operational excellence, technology and the business and delivery model need to be in sync to deliver high-quality service. There is also an issue of enforcing accountability as it gets distributed across many stakeholders in a teleradiology company.
Ensuring the security and privacy of patient data during transmission and storage is also a concern, as sensitive medical information is at risk of unauthorized access or breaches.
Variations in imaging equipment and protocols often result in inconsistent image quality. These differences increase the chance of misinterpretation. Standardization of imaging procedures is crucial. It's critical to have diagnostic-quality images before transmission.
With a growing demand for radiology services, how can we address the shortage of radiologists?
The field of radiology faces a shortage of specialists. Radiology needs smarter solutions driven by technology. Tools like teleradiology, PACS, cloud platforms and AI can transform how radiologists work, making collaboration among stakeholders seamless and efficient. These technologies give radiologists the power to access imaging studies and diagnostic tools anytime and anywhere, reducing the limitations posed by geographic and time constraints.
Cloud-native systems, in particular, offer highly adaptable and accessible platforms, enabling radiologists to review and analyze images remotely. This allows smaller private practices, large hospitals, and multi-centre imaging facilities alike to optimize their existing workforce and meet growing demands. By facilitating real-time collaboration and efficient workflows, technology ensures better utilization of radiologists' expertise, ultimately mitigating the impact of the supply-demand mismatch in radiology services.
What role do you think continuous education and upskilling play in the radiology profession today?
As the radiology field is dynamic and undergoes a rapid transformation that is driven by AI, ML, VR, and 3D modelling, the need for continuous education and upskilling is very important in the radiology profession. These technologies revolutionize how radiologists diagnose and treat patients, bringing new opportunities. However, they also add complexities. Radiologists face more challenges now. The digital revolution has sparked the growth of sub-specializations in radiology, which demands a higher degree of expertise in areas like neuroimaging, interventional radiology, and musculoskeletal imaging.
Radiologists remain at the forefront by continuously learning. They attend clinical meetings, conferences and special training programs to improve themselves. Learning about AI diagnostic tools or advanced imaging methods enhances a doctor's efficiency. This keeps patients safe by reducing diagnostic errors. Sub-specialization should be encouraged based on their personal interests and offer precise guidance. Radiologists should blend their skills with new AI technologies as AI becomes more common in practices. This mix keeps radiology focused on accuracy, efficient work and patient-centred care.
How is radiology contributing to early disease detection, especially in oncology and cardiology?
Radiology plays a key role in the early detection of diseases by using advanced imaging techniques that provide accurate and quick diagnoses, especially in critical fields like oncology and cardiology. For instance, mammography and digital tomosynthesis have changed how doctors detect breast cancer early. Imaging techniques such as angiography and coronary CT angiography help cardiologists detect arterial blockages and other heart-related issues much more accurately. These advancements in radiology have tremendously helped clinicians detect diseases at earlier stages, where interventions are most effective.
At The Insight Tribe, we once successfully implemented a strategy that leveraged AI in MR imaging, that enhanced the diagnosis of endometriosis which is a significant women’s health issue. By analyzing MR images with innovative AI algorithms, We achieved greater diagnostic accuracy by studying MR images with innovative AI algorithms which led to better surgical planning and treatment.
AI's capabilities go beyond automation. It offers predictive analytics by studying past and current data to identify risk factors and disease patterns. This power helps move toward personalized medicine, where AI tailors treatment plans based on an individual’s unique medical history and characteristics. By combining advanced imaging with AI in radiology, it'll not only detect diseases earlier but also deliver precise diagnostics and tailored treatment strategies, that greatly improves patient outcomes.
What are the benefits of investing in regular screening programs for public health?
Screening programs stand as a foundation of today's public health and preventive medicine. It enables early detection of any risk factors or diseases in individuals who show no symptoms. The fundamental benefit of these programs is that they allow for timely intervention, reducing the risks, complications, and costs associated with late-stage diagnosis. Screening plays a key role in controlling outbreaks of contagious diseases. It also focuses on treating chronic illnesses such as cancer, diabetes and cardiovascular conditions.
In India, the significance of regular screening programs cannot be overstated. The Government of India has been implementing the National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS) since 2010 under the National Health Mission. This initiative aims to reduce the burden of non-communicable diseases (NCDs), which account for over 60% of deaths in the country. Screening at the district level has enabled early diagnosis and treatment, particularly in rural and underserved areas, where healthcare access remains a challenge.
The integration of AI and digital health technologies into screening programs can further amplify their impact. AI-powered diagnostic tools enable faster, more accurate screenings, even in resource-constrained settings, while digital health platforms ensure better follow-up and data-driven decision-making.
Can you share your thoughts on the latest advancements in MRI, CT, and ultrasound technologies?
The latest advancements in MRI, CT, and ultrasound technologies are transforming diagnostic imaging by enhancing precision, accessibility, and patient care. Low-field-strength MRI scanners offer economical alternatives for routine use, the helium free MR is more sustainable , lowers operational cost and is easier to install , Innovations like 3D curved planar reformation and cinematic rendering provide detailed and intuitive visualizations, improving clinical decision-making. Lot of AI applications in MR especially in reconstructing the images are saving time allowing hospitals and imaging centers to do more per machine
In CT technology, photon-counting detector CT scanners lead the way with lower radiation doses, higher resolution, and better tissue contrast compared to traditional scanners. Dual-energy CT and advancements in musculoskeletal imaging, such as MR neurography, further push diagnostic boundaries by addressing challenging imaging scenarios. Similarly, ultrasound innovations, including high-resolution transducers and portable AI-assisted devices, are making diagnostics more accessible, especially in underserved areas.
From an Indian perspective, accessibility and affordability are paramount. During my time at GE, I contributed to the Make in India CT project, which produced cost-effective, locally manufactured CT scanners, increasing access to advanced imaging nationwide. These advancements, coupled with strategic initiatives, are crucial for ensuring cutting-edge technologies benefit broader populations while addressing the growing healthcare demand.
How are portable imaging solutions shaping the future of radiology services?
Portable imaging solutions are transforming radiology by enabling faster diagnosis, better patient care, and improved infection control, especially in public health and TB screening. Advances in technology have miniaturized devices like X-ray, ultrasound, CT, and MRI scanners, making them compact and portable. For example:
Portable MRI: Offers point-of-care (POC) imaging for critical conditions like brain injuries, delivering detailed images directly at the bedside.
Mobile X-Ray: Lightweight and battery-powered devices enable bedside imaging, with wireless data transmission. Applications include diagnosing pneumonia, fractures, lung cancer, heart diseases, and pediatric conditions.
Mobile Ultrasound: Handheld or cart-based systems use AI for real-time optimization of imaging settings, making them versatile for both superficial and deep anatomy scanning.
Future innovations may include AI-enhanced color imaging for better visualization, potentially replacing traditional grayscale images with more informative, labeled visuals.
How does radiology integrate with precision medicine to offer personalized treatment plans?
In incorporating precision medicine, radiology plays a vital role, also enabling highly personalized treatment plans. Advanced medical facilities not only help in accurate diagnosis and treatment monitoring but also generate rich data that assists in projecting analytics through AI.
With the combination of imaging studies with genomic and clinical data, healthcare providers can discover unique disease characteristics, such as cancer subtypes, that are more accurate. This approach allows therapies to be modified based on a patient’s genetic profile, improving outcomes and minimizing the risk of any fault in treatment. As medical intelligence grows, radiology’s collaboration with the particular medicine is set to further enhance diagnostic accuracy and transform patient care.
What challenges do radiologists face when leveraging large datasets for diagnostics?
Radiologists face significant challenges while influencing large datasets for diagnostics. The Primary issue is the lack of high-quality, standardized data, as variations in imaging procedures and explanations hamper the development of dependable AI models. Additionally, biases in data complicate this, as many datasets lack variety, leading to inconsistent AI performance across different demographics and raising concerns about diagnostic impartiality. In addition, as many models operate as "black boxes", the understanding of AI systems remains a challenge, with ambiguity about the decisions becoming a potential trust issue.
It is a sensitive task, to ensure patient privacy while complying with strict data-sharing laws. Ethical and dictatorial concerns also add difficulty. Also, the cost of implementing AI solutions and limitations in existing hardware, particularly where there is an unavailability of healthcare operators, make processing large datasets a significant obstacle. To overcome these challenges needs joint efforts to improve data standardization, enhance AI transparency, and make advanced tools handier.
What are the ethical considerations surrounding AI adoption in radiology?
To ensure its responsible and equitable use, the adoption of AI in radiology brings critical ethical considerations. Ensuring unbiased and reliable data is vital, as biased training data can lead to imbalanced diagnostic outcomes and grind down trust. AI models must also be regularly set up to reflect new data and evolving clinical needs, maintaining fairness and reliability.
As many AI systems operate as "black boxes," making their decision-making processes difficult to understand, transparency and accountability are ultimate. Ethical frameworks stress the need for "explainable AI," allowing radiologists, as the “human-in-the-loop,” to validate and trust AI recommendations to improve patient safety.
In addition, patient autonomy and data privacy must be protected, with AI systems complying with severe conventions. It’s important to provide education and allow clear policy development, which are essential to guide implementation, and also ensure radiologists understand the capabilities and limitations of AI. Addressing these considerations builds trust and ensures AI augment radiology without compromising fairness or safety.
How do you see regulatory frameworks evolving to keep pace with technological innovations?
Taking charge of the frameworks must advance dynamically to address the rapid pace of technological innovations, particularly in AI-powered healthcare solutions. Future regulations are likely to focus on adaptive models, allowing phased approvals and real-world performance monitoring to ensure safety and efficacy. This approach will enable technologies to scale while remaining responsive to emerging challenges. Also, global synchronization of standards will play a crucial role in streamlining approvals across borders and nurturing trust in innovative solutions.
Key priorities will include ensuring transparency, fairness, and accountability by mandating unbiased datasets and requiring AI tools to provide interpretable outputs for clinicians. Regulations will also emphasize robust data privacy and security measures, addressing concerns about patient data usage.