Telefonica Tech Deploys AI to Screen Lung Cancer in Spain

In July 2026, Harrison.ai expanded European-certified clinical decision support software across regional health services to help doctors rapidly prioritize urgent cases. Telefónica Tech has expanded the deployment of clinical decision support software across regional health services to help doctors rapidly prioritize urgent cases. It partnered up with global healthcare technology company Harrison.ai, which has deployed an AI diagnostic tool across Spain's regional health services. The software analyses chest X-rays to detect up to 124 clinical findings. These include pulmonary nodules that could also potentially indicate lung cancer.

According to Harrison.ai, the algorithm was trained on data from more than 780,000 chest X-ray studies, with at least three qualified radiologists independently labelled each study during development. The system holds Conformite Europeenne (CE) IIb medical device certification. This represents the highest level of assurance for diagnostic support tools in the European Union (EU), permitting use in potentially lethal health conditions. It also confirms compliance with EU legislation governing safety, efficacy, and quality for medical devices.

The AI tool integrates directly into existing healthcare information technology (IT) systems and radiologists’ workflows. According to Telefónica Tech, this approach allows customization to match technical and clinical requirements. Radiologists can use the system to categorize cases by urgency, with the aim of prioritizing time-critical patients within their existing patient lists. The technology could also help reduce diagnostic response times by streamlining workflows and helping detect diseases at much earlier stages.

Impact on the Healthcare Market

Artificial intelligence (AI) has emerged as a revolutionary force in the healthcare industry, particularly in the field of pulmonary diagnostics. Pulmonary diagnostics consists of a range of medical procedures that help to assess the health and function of the respiratory system, particularly the lungs. These diagnostics include imaging techniques such as X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI), as well as pulmonary function tests and bronchoscopy. Accurate and efficient lung imaging is vital for the early detection, diagnosis, and management of various pulmonary conditions, including lung cancer, pneumonia, chronic obstructive pulmonary disease (COPD), and interstitial lung diseases (ILDs).

Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering innovative solutions to improve diagnostics, treatment, and patient outcomes. In the field of pulmonary imaging, AI algorithms and machine learning techniques have shown promising results in automating image analysis, detecting abnormalities, and predicting disease prognosis.

Impact on the Lung Cancer Surgery Market

The global lung cancer surgery market size was estimated at USD 1.55 billion in 2025 and is projected to increase from USD 1.64 billion in 2026 to approximately USD 2.76 billion by 2035, growing at a CAGR of 5.95% from 2026 to 2035.

According to Precedence Research, the rising adoption of minimally invasive surgeries, including robot-assisted and video-assisted techniques, is helping in reducing health risks, shortening recovery times, and minimizing hospital stays, thus driving high market demand. One of the most significant advancements in pulmonology is robotic-assisted bronchoscopy. This technique is revolutionizing the way pulmonologists diagnose and treat lung cancer, which is one of the leading causes of cancer-related deaths worldwide.

Another potential area of research is the development of AI-driven robotic systems that can perform certain tasks during bronchoscopy or surgery, with the help of a human operator. These systems could potentially improve the speed and accuracy of procedures while also reducing the load on clinicians.

Impact on the Artificial Intelligence in Diagnostics Market

The global artificial intelligence in diagnostics market size was estimated at USD 1.94 billion in 2025 and is anticipated to reach around USD 11.82 billion by 2035, expanding at a CAGR of 19.81% from 2026 to 2035.

According to Precedence Research, the use of deep learning algorithms and AI tools in diagnostics can improve the accuracy, speed, and efficiency for diagnosing patients with minimal errors. The introduction of AI and machine learning tools in diagnostics is transforming the healthcare industry by supporting doctors in advanced disease diagnosis and providing personalized treatments to patients with better judgments and much quicker results.

The development of advanced information systems with the help of cloud computing and AI integrated tool for storing large datasets, EHRs, and demographic trends for upgrading AI systems to provide accurate results in disease diagnosis as well as disease prognosis. The rise in investments and funding by public and private sectors, as well as the collaborations among industries, research institutions, and academia for advancing the applications of AI tools in diagnostic procedures, is also expected to create opportunities for growth in the market during the forecast period.

Expert Opinion

• Carlos Martínez, Head of Data and AI at Telefónica Tech, said:

“With this new solution, which we are already offering to several regional health services, we are continuing to expand the services we offer our clients and making progress towards our aim of becoming the best gateway for citizens, businesses, and public administrations to access digital technologies.”

• Dean Bubley, Founder and Director of Disruptive Analysis, said:

“This aligns with some other (mostly Asian) telcos that are pursuing ‘AI Factory’ strategies more geared towards vertical AI solutions, either for B2B or B2C sectors,” he wrote. He described the integration approach as exactly right.

“No enterprise is going to listen to an arriving telco on its doorstep and suggest a complete re-architecting of its IT (and especially AI) portfolio around network-based compute,” he adds. “That doesn't mean that some workloads, in some places, couldn't also benefit from more connectivity. I'm sure that shifting big X-ray images and data around will indirectly drive network upgrades in some places as well.”