AI-Derived Software in Chest X-Ray Analysis: A NICE Perspective

By HEOR Staff Writer

October 10, 2023

A recent update has been published by NICE (National Institute for Health and Care Excellence). It described the use of artificial intelligence (AI)-derived software in analysing chest X-rays for suspected lung cancer. This has been under scrutiny in primary care referrals. This initiative is part of the NHS’s ambition to diagnose 75% of all cancers at stages 1 or 2 by 2028.

Potential Benefits of AI-Derived Software in Chest X-Ray Analysis

The AI-equipped software can detect lung abnormalities in chest X-ray images automatically. It can assist radiologists and reporting radiographers in interpreting these images. This aids in making clinical decisions about the need for further investigations or CT scans. The software can distinguish between normal and abnormal images. It can also highlight suspected abnormalities. It allows for prioritizing the review of chest X-rays. This could potentially accelerate the referral process to CT scans. 


However, the committee noted that trust in AI-derived software for patients and healthcare professionals was crucial for its efficient use. This would require standardisation of technologies and further research in the setting of interest. Prospective studies need to be conducted in a population referred from primary care to reflect how the NHS would use the software in clinical practice. 


The committee also highlighted that AI-derived software could be particularly beneficial for certain groups. It could improve lung cancer detection in people with underlying lung conditions. These include asthma, chronic obstructive pulmonary disease (COPD), people with a family background of lung cancer, and younger women who do not smoke.

Potential Risks and Challenges in the Use of AI-Derived Software

However, the committee also pointed out potential risks associated with using AI-derived software. These include the cost of the AI-derived software. This may not be offset by cost and resource savings later in the pathway. Lower specificity to detect cancerous nodules and other abnormalities that suggest cancer could result in more people without cancer having CT scans. This could have cost and disutility implications.

In conclusion, while AI-derived software shows promise in aiding the early detection of lung cancer, more research and standardisation are needed to ensure its efficient and effective use in clinical practice. You should also carefully consider the potential benefits and risks associated with its use.

Reference url

Recent Posts

Obesity Health Economics: Forecasting Trends and Costs in the US

By HEOR Staff Writer

October 29, 2025

Obesity health economics reveals a pressing public health crisis in the US, where rising prevalence drives massive costs and strains healthcare systems. If you're wondering how obesity impacts the economy, consider this: projections show annual expenses could surpass $1 trillion by 2040, fueled b...
AI Chatbot Delusions: Navigating the Risks of Validation in Mental Health

By João L. Carapinha

October 28, 2025

A BMJ article explores the potential for AI chatbot delusions to validate or induce delusional thinking. Emerging evidence shows that individuals with and without previous psychiatric histories have reported distressing delusions after extensive chatbot interactions. It remains uncertain if AI di...
Challenging the Narrative: Pharmaceutical Innovation Funding and Its Complex Dynamics

By João L. Carapinha

October 27, 2025

Pharmaceutical innovation funding in the UK faces scrutiny amid industry claims that low NHS spending deters investments, but this narrative overlooks key drivers like scientific talent, tax incentives, and operational efficiencies rather than drug prices alone. A recent Lancet article critiques ...