Artificial Intelligence (AI) is transforming the field of radiology, offering opportunities to improve diagnostic accuracy, optimize workflows, and confront the global shortage of radiologists. The global market for AI in healthcare, and radiology specifically, is on an accelerated growth trajectory, estimated at USD 19.27 billion in 2023 and is expected to grow at a CAGR of 38.5% from 2024 to 2030. This expansion is driven by several factors: the increasing patient demand, the surge in medical imaging studies, and the critical need to address the shortage of radiologists.
Globally, the radiology workforce faces a shortage of approximately 20% in developed countries and up to 80% in low- and middle-income regions. The American College of Radiology (ACR) highlights that the radiologist shortage is intensified by an anticipated increase in imaging volumes, expected to surpass 5 billion studies annually by 2025. This imbalance has led to increased costs, longer turnaround times, and significant burnout among radiologists. Dr. Eliot Siegel, a prominent radiology thought leader, states, “AI has the potential to transform radiology by providing tools that not only enhance the accuracy of interpretations but also help address the growing demand for imaging services.”
In an effort to overcome the imbalance and improve patient outcomes, over 200 companies across the globe have developed niche AI applications aimed at enhancing radiologists' efficiency, accuracy, and productivity. However, the fragmented nature of these solutions has created a complex and challenging ecosystem for healthcare providers to navigate, with new FDA-cleared AI vendors emerging regularly. In response to this challenge, platforms like CARPL have emerged as vital solutions. CARPL, the world’s first comprehensive testing, deployment, and monitoring platform for radiology AI, integrates over 125 AI applications from nearly 50 vendors into a single user interface. This unified platform simplifies data integration and procurement, allowing radiologists to fully leverage the diverse AI ecosystem without the burden of managing multiple siloed solutions.
As AI technology continues to evolve, its impact on radiology practice is becoming increasingly significant, shaping the future of medical imaging and patient care globally. The driving force for radiology AI adoption across the United States and Brazil is defined by increasing imaging volumes and the need to optimize workflow efficiency, while Singapore and India prioritize public health initiatives and equitable access to care. It is crucial to recognize the various strategies for AI adoption in radiology worldwide, understand the use cases that highlight the necessity for integrating AI into radiology practices, and examine the effects on local healthcare systems and patient populations.
A leading force in the adoption of radiology AI, the United States represents advanced healthcare infrastructure and substantial investment in technology. The American College of Radiology (ACR) reports that around 60% of radiology departments in the U.S. have either adopted AI solutions or are evaluating their implementation. Similarly, the Brazilian healthcare system, the largest in Latin America, handles a substantial volume of diagnostic imaging. Over 30% of Brazilian radiology departments are currently using or evaluating AI solutions, with this number expected to rise as technology becomes more accessible and affordable.
At University Hospitals in Cleveland and FIDI in Brazil, the deployment of radiology AI applications in emergency settings has notably improved triage and workflow efficiencies. At University Hospitals, the CARPL radiology AI platform, integrated in June 2023, processed over 8,000 appendicular skeletal radiographs, resulting in a 30% increase in fracture detection rates and significantly enhanced triaging efficiency. This AI integration allowed for real-time analysis and seamless incorporation into the existing PACS, reducing radiologists' workload and speeding up patient care. Similarly, FIDI, a major diagnostic imaging provider in Brazil, manages over 250,000 images monthly across its 82 sites, including 10 hospitals in São Paulo. The implementation of the CARPL platform enabled the automatic routing of over 4,500 studies for inferencing, streamlining diagnostic workflows and providing detailed performance reports. By integrating with Philips PACS, FIDI optimized its emergency department operations, prioritizing abnormal cases and reducing review times for normal cases, which improved the overall speed and accuracy of patient care.
Across the globe, Singapore faces significant challenges due to an aging population and increasing prevalence of chronic diseases. The demand for healthcare services is rising sharply, necessitating advanced solutions to sustain quality care and manage resource constraints effectively. Similarly, the Indian healthcare system grapples with a large patient population, a shortage of radiologists, and disparities in healthcare access across urban and rural areas. The Indian government and various organizations are investing in AI-driven solutions to address critical public health issues such as tuberculosis (TB), cancer, and musculoskeletal disorders. The integration of AI into radiology practice is expected to alleviate some of the burdens on the healthcare system.
Synapxe, the public healthcare IT agency in Singapore, plays a pivotal role in digitizing and integrating Singapore’s health ecosystem. It supports numerous public healthcare institutions and collaborates with private sector partners to advance AI applications in medical imaging. A notable example of AI adoption is Synapxe's collaboration with CARPL.ai, NTT DATA, and DeepTek to develop AimSG, the region’s first common AI Medical Imaging Platform. AimSG supports the integration of AI models from multiple vendors or in-house AI solutions, addressing the challenges faced by Singapore’s healthcare system. CARPL.ai facilitated the deployment of AI solutions across public hospitals, including Singapore General Hospital (SGH) and Changi General Hospital (CGH). Over six months, AimSG processed approximately 40,000 cases, significantly contributing to the efficiency and effectiveness of radiological services. The deployment of AI at SGH and CGH demonstrated around 700 cases processed daily, with a turnaround time of 45 seconds to one minute per case.
India faces a critical challenge with tuberculosis (TB), the leading cause of death globally. According to the World Health Organization (WHO), India reports the highest burden of TB, with two deaths occurring every three minutes in 2022. This high burden is exacerbated by insufficient screening programs and a scarcity of radiologists in remote areas. The Indian government's National Tuberculosis Elimination Program (NTEP) has set ambitious targets to reduce TB incidence and mortality. However, gaps remain, particularly in remote and underserved regions where access to diagnostic services is limited.
In response to the challenges posed by TB, the William J. Clinton Foundation (WJCF) and the Clinton Health Access Initiative (CHAI) implemented a project leveraging AI and portable X-ray machines to improve TB screening in high-risk communities. The AI-enabled system runs on laptops that function offline, without requiring constant electricity or internet access, making it ideal for use in challenging environments. The CARPL platform demonstrated significant impact, with a Number Needed to Screen (NNS) of 109, indicating a substantial improvement in screening efficiency compared to the previous year's NNS of 2,824. This performance is well below the national target of 1,538, showcasing the effectiveness of AI in enhancing screening programs.
Across the globe, the integration of AI into radiology workflows is proving to be a transformative force, reshaping how healthcare systems manage imaging volumes, enhance diagnostic precision, and address critical shortages in medical expertise. As Dr. Michael Apkon noted, "AI has the potential to make diagnostic processes faster, more accurate, and more efficient, allowing healthcare professionals to focus on what they do best—providing care." This is particularly evident in the diverse case studies explored, where AI’s impact goes beyond mere optimization—it’s about enabling a future where healthcare delivery is faster, more accurate, and accessible to all.
What becomes clear is that the future of radiology lies in the strategic, thoughtful application of AI, tailored to meet the specific needs of each healthcare environment. Platforms like CARPL are crucial in this journey, offering a cohesive solution that unites diverse AI applications under one roof, thus simplifying the complexities of AI adoption. As AI continues to evolve, its role in radiology will undoubtedly expand, driving not just incremental improvements but system-wide transformations in how we diagnose, treat, and care for patients worldwide.
