06-08-2025
UAE: AI outperforms human radiologists, records zero false negatives
A comprehensive study on an AI model developed by Abu Dhabi-based healthcare group M42 has found that it can safely and accurately detect tuberculosis from chest X-rays — outperforming human radiologists.
Published in the prestigious scientific journal npj Digital Medicine – Nature, the study conducted in collaboration with Abu Dhabi's Capital Health Screening Centre (CHSC) is among the largest real-world clinical validations of an AI-driven healthcare solution to date, analyzing over one million chest X-rays to evaluate the efficacy and scalability of AI in TB screening.
The study assessed AI Radiology in Screening TB (AIRIS-TB), M42's cutting-edge AI model engineered to streamline routine tuberculosis screenings, allowing radiologists to focus on more complex or urgent cases. The model demonstrated exceptional performance, indicating high diagnostic accuracy and an unprecedented 0 percent of false negatives.
This means that the model has the potential to safely automate up to 80 percent of routine chest X-ray assessments, directly reducing the workload of radiologists, minimising the risk of human error, and delivering significant cost efficiencies in high-throughput low-prevalence settings.
Labour-intensive
Currently, reviews of chest X-rays remains labor-intensive and prone to oversight and error, potentially leading to missed or delayed diagnoses. A prior study has indicated a 26.6 percent increase in missed findings when radiologists double their annotation speed, and a rise in errors after 9 hours into their shift.
AIRIS-TB delivered consistently strong performance across a wide range of demographic groups, including variations in gender, age, HIV status, income levels, and a diverse population covering six World Health Organization (WHO) regions, highlighting the model's robustness, fairness and generalizability across diverse global populations.
These results indicate the model's potential to significantly improve clinical workflows and drive earlier, more equitable screening of TB in high-volume programs worldwide.
"This landmark study marks a pivotal moment in the potential power of AI in the global fight against tuberculosis,' said Dimitris Moulavasilis, Group CEO of M42. 'Our AIRIS-TB model stands as a compelling testament to the unmatched accuracy, safety and scalability that AI can deliver, particularly in resource-limited settings where there is a shortage of radiologists and the need to tackle TB is greatest.'
He added that in regions with a high prevalence of TB, the model offers a 'scalable technological solution' that can help save lives. 'These results signal the transformative role AI can play in reshaping global public health and redefining how healthcare is diagnosed, delivered and experienced worldwide,' he said.
The study underwent rigorous peer review and ethical oversight by the Department of Health – Abu Dhabi, ensuring transparency, accountability, and the highest standards of clinical integrity. Its publication in a leading scientific journal reinforces M42's position as a global pioneer in AI-led health solutions and solidifies the UAE's growing prominence as a global, data-driven hub for cutting-edge medical innovation and technology.
