Artificial Intelligence (AI) is on the verge of ushering in a new era of healthcare where AI-powered medical imaging boosts diagnostic value and efficiency, and empowers clinicians to make more precise decisions.
Medical imaging companies like Philips are at the forefront, integrating AI into advanced medical devices such as MRI and CT scanners to improve diagnostic speed and precision1Philips. (s. d.). MR SmartSpeed fast imaging technology. https://www.usa.philips.com/healthcare/resources/landing/smartspeed.
Public institutions aren’t far behind, including National Health Service in the UK, which is trialing AI-enabled breast cancer screening aimed at detecting cancer earlier and more accurately for up to 700,000 women22. Largest trial of AI in breast cancer screening launched. Venkatesan, Priya. The Lancet Oncology, Volume 26, Issue 3, 285. Beyond diagnostics, AI is even helping clinicians to make more precise decisions in surgery by visualizing vertebral structures for surgical spine planning, without exposing the patient to CT radiation3Rommelspacher, Y., Schulte, A. P., Tanner, S., Schellhammer, F., Kling, S., Seevinck, P., Sangüesa, M. G., & Strauss, A. C. (2025). Evaluation of MRI technologies for surgical spine planning and navigation. European Spine Journal. https://doi.org/10.1007/s00586-025-08703-5.
These advancements underscore AI’s potential to alleviate pressures on healthcare systems and improve patient care. However, there are still some outstanding questions: What will deployment of AI look like at the clinical level? What is the appetite for adoption by healthcare providers (HCPs)? And over what time frame are we expecting the shift towards AI adoption to occur? In this article, Alcimed explores these critical questions, and aims to provide key insights for healthcare organizations navigating this technological evolution.
So far, a prominent mechanism for AI deployment into clinics has been via medical imaging companies developing first-party algorithms that are then integrated into their technology portfolios. Companies including Philips, Siemens Healthineers, and GE Healthcare are all working on AI built into their scanners that assist clinicians by improving image quality and speed, with the goal of boosting the diagnostic value of medical images and improving confidence in medical interventions1AI-Rad Companion. (s. d.). Siemens Healthineers USA. https://www.siemens-healthineers.com/en-us/digital-health-solutions/ai-rad-companion2SIGNA MR Vision. (s. d.). GE HealthCare (United States). https://www.gehealthcare.com/products/magnetic-resonance-imaging/signa-mr-vision. By creating proprietary AI solutions tailored to their equipment, these companies seek to make clinical integration easy, reliable, and comprehensive. As AI adoption grows, competition will emerge among these companies based on their integrated AI ecosystems. The leading provider will be the one that offers clinicians the most functional and high-quality algorithms, such as image segmentation, morphology quantification, and diagnostic support.
An alternative scenario for artificial intelligence adoption is the emergence of “independent” developers creating AI algorithms for medical imaging. While major companies are creating task agnostic algorithms (i.e. focused on improving the imaging technology itself), smaller third-party developers are taking a different approach by creating algorithms specialized in particular tasks to maximize their performance, ex. tumor detection3Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., Wang, F., Peng, Y., Zhu, J., Zhang, J., Jackson, C. R., Zhang, J., Dillon, D., Lin, N. U., Sholl, L.,. . . Yu, K. (2024). A pathology foundation model for cancer diagnosis and prognosis prediction. Nature, 634(8035), 970‑978. https://doi.org/10.1038/s41586-024-07894-z. The goal for these developers is for clinics to choose their algorithms based on seeking the highest level of performance for a particular use case. These algorithms are then integrated into the Radiology Information System (RIS) and the Picture Archiving and Communication System (PACS) of clinics to make deployment seamless. In this scenario, the long-term outcome would be the dominance of emerging middleware platforms, which act as an “app store” of algorithms for clinics to freely select, where multiple winners can exist by becoming the highest quality algorithm for a certain use case.
The future of AI deployment may ultimately lie somewhere in the middle, with each scenario occurring at differing rates of prevalence, hybrid models allowing partnerships between equipment manufacturers and middleware platforms, or independent AI developers directly.
While AI deployment into clinics is ongoing, the next hurdle AI players will need to overcome is the willingness of clinicians to adopt AI into their current workflows. Some clinicians express concern about AI’s reliability and its potential to replace human reasoning. Others express concern about the integrity of clinical patient data, and about the potential for AI to replace them in the clinic entirely.
However, the popular view for AI is one as a safe and robust complementary tool that enhances diagnostic accuracy and efficiency, similar to other tools that have entered the clinic. For instance, at the Hospital Universitario de A Coruña (CHUAC) in Spain, AI has been integrated into radiodiagnostic services to improve precision and speed, particularly in emergency settings4Ser, C. (2024, 26 novembre). Cadena SER. Cadena SER. https://cadenaser.com/galicia/2024/11/26/la-inteligencia-artificial-revoluciona-el-radiodiagnostico-en-el-chuac-radio-coruna/. Similarly, Northwell Health in New York has developed an AI tool called iNav to detect pancreatic cancer earlier by analyzing MRI and CT scans taken for unrelated issues, reducing the time from diagnosis to treatment by 50%5Wilser, J. (2024, 30 octobre). Catching cancer early. TIME. https://time.com/7094712/northwell-health-inav/. These examples demonstrate that when AI tools are designed to support rather than replace clinicians, there is a greater willingness from clinicians to take the time to learn the equipment and adopt them into their workflows.
Furthermore, the deployment of AI has been shown to reduce diagnostic errors and improve patient outcomes, fostering increased trust and acceptance among healthcare professionals. Education initiatives such as workshops, seminars, and conferences can also be a powerful tool for developers. As AI technology continues to evolve, ongoing collaboration between developers and clinicians will be essential to address concerns and ensure seamless adoption into clinical workflows.
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In particular, AI is making significant inroads into cardiology and oncology imaging, leading to notable improvements to patients’ care quality. In cardiology, AI algorithms assist in analyzing echocardiograms and detecting arrhythmias, enhancing diagnostic accuracy and efficiency.
For instance, researchers at the University of Texas at San Antonio are developing AI algorithms to advance coronary imaging, offering real-time assessments of heart health for preventive care6Hand-in-hand : UTSA, UT Health San Antonio researchers use AI to improve real-time coronary imaging. (s. d.). UTSA Today. https://www.utsa.edu/today/2024/01/story/utsa-ut-health-san-antonio-use-AI-for-coronary-heart-disease.html. In the industry, Nanox.AI utilizes AI to identify asymptomatic and undetected chronic conditions, such as coronary artery calcification, by analyzing routine CT scans, using FDA-approved algorithms7Nanox. (2025, 17 février). Nanox.AI – Nanox. https://www.nanox.vision/ai/#broad-suite-of-fda-510k-cleared-solutions.
Meanwhile in oncology, AI is assisting in early cancer detection and treatment planning. AI models like CHIEF, developed by Harvard Medical School, have demonstrated high accuracy in detecting various cancer types and predicting survival rates, potentially reducing the need for expensive DNA sequencing8Williams, R. (2025, 6 février). Prostate cancer and AI : the exciting advances that could transform treatment. The Guardian. https://www.theguardian.com/research-to-reality/2025/jan/24/prostate-cancer-and-ai-the-exciting-advances-that-could-transform-treatment. Researchers at Queen’s University Belfast are deploying AI to analyze digital pathology images, predicting cancer behavior and response to hormone therapy, thereby advancing personalized treatment strategies for prostate cancer patients9Pesheva, B. E. (2024, 12 décembre). A New Artificial Intelligence Tool for Cancer. Harvard Medical School. https://hms.harvard.edu/news/new-artificial-intelligence-tool-cancer.
Cardiology and oncology stand out in AI adoption due to a combination of medical urgency and market size. In contrast, fields like orthopedics lag behind due to fewer life-threatening conditions and a lower volume of imaging-driven decision-making. AI adoption in these areas may increase as AI capabilities expand towards surgical planning, predictive modeling for patient outcomes, and personalized rehabilitation strategies.
Despite the budding advancements of AI, widespread adoption of AI in medical imaging is expected to take place as a gradual shift that will occur over the next decade. According to Precedence Statistics, the global market for AI in medical imaging was valued at USD 1.28 billion in 2024 and is projected to reach around USD 14.46 billion by 2034, with hospitals and clinics accounting for the highest market share (64.80% in 2024)10Statistics, P. (2025, 13 février). AI in Medical Imaging Market Size Projected to Reach USD 14.46 Bn By 2034. GlobeNewswire News Room. https://www.globenewswire.com/news-release/2025/02/13/3026027/0/en/AI-in-Medical-Imaging-Market-Size-Projected-to-Reach-USD-14-46-Bn-By-2034.html#:~:text=The%20global%20AI%20in%20medical,27.10%25%20from%202025%20to%202034.&text=Ottawa%2C%20Feb.. This trend indicates a large appetite for AI technology in clinics, which is displayed by a biannual doubling of the total market size of AI in healthcare clinics.
With this trajectory, we expect to see several important milestones in the next 2 to 5 years including: expanded regulatory approval, technical integration with existing healthcare infrastructure, more clinical evidence of AI’s impact on patient outcomes, formation of clinical training initiatives, and increased clinician trust.
Engaging with experienced consultants can guide both healthcare organizations and AI developers through the complexities of AI integration, ensuring alignment with clinical objectives and regulatory requirements. Nautilus.ai, Alcimed’s Data & AI team is ready to support you in exploring these opportunities, don’t hesitate to contact our team!
About the authors,
Steven, Senior Healthcare Consultant at Nautilus.ai, Alcimed’s Data & AI team
Matthieu, Manager at Nautilus.ai, Alcimed’s Data & AI team
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