The future’s bright, the future’s Orange AI… If you Google ‘how much is AI worth?’, the AI overview tells you “The global artificial intelligence (AI) market size is estimated at approximately $244 billion to $391 billion in 2025 and is projected to reach over $3.4 trillion by 2033.” The uptake of AI in all aspects of our life is rising at an exponential rate – in fact, this week saw three AI-generated songs hit the top spots in Spotify and the American Billboard charts. AI in modern medicine is weaving its way into contemporary practice, evidenced by the fact that two of the articles in this issue’s journal reviews are focused on this very subject. The two editors’ choices look at the role of AI in swallowing assessment with videofluoroscopy and AI in the diagnosis of obstructive sleep apnoea. Both articles find that use of AI is a mixed bag – some aspects of diagnosis are very good. But it’s not good at the whole process, due to issues around the decision-making process in AI models, validation against diverse patient groups and challenges with integration of AI in routine work. Like it or loathe it, AI is clearly here to stay and will be a part of all our future practices. However, bearing in mind the words of the CEO of Alphabet (Google’s parent company) in a BBC interview in November, who stated that people should not “blindly trust” everything AI tools tell them, humans will always be needed in healthcare! As always, the editors would like to thank all of our journal reviewers for their contributions.
Nazia and Gayno
Obstructive sleep apnoea (OSA) affects adult and paediatric patients. It is associated with significant morbidity, reduces the quality of life and is even associated with premature deaths. The most reliable method to diagnose the condition currently is polysomnography. It requires investment in equipment and resources. Research is ongoing to develop faster and easier screening tools to diagnose OSA. The authors of this scoping review attempted to provide an overview of OSA diagnosis using artificial intelligence (AI). They conclude that although AI has shown high accuracy in some specific studies, several challenges need to be overcome. These include transparency of the decision-making process in the model, inadequate validation across diverse patient groups and challenges with integrating AI tools in routine clinical workflows. Standardising implementation protocols in the healthcare systems may help to address some of the challenges. As we all know, AI is here to stay!
