Abstract
This paper presents the strengths, weaknesses, opportunities, and threats for Artificial Intelligence (AI) as applied to long-term respiratory disease management. This analysis will help to identify, understand, and evaluate key aspects of the technology as well as the various internal/external forces which influence its success in this application space. Such understanding is instrumental to ensure judicial planning and implementation with suitable safeguards being considered. AI has the potential to radically change how respiratory disease management is conducted and may help clinicians to realise new treatment paradigms. The application of AI is clearly not specific to respiratory disease management; however it is a chronic disease that requires on-going monitoring and well evidenced decision making regarding treatment pathways or medication modification. This work emphasises the current position of AI as applied to respiratory disease management and identifies the issues to help develop strategic directions to ensure successful implementation, evidenced by ubiquitous acceptance and uptake.
Highlights
As medical technology gets smaller, smarter, and more able to process large volumes of data, there are opportunities to take devices which are hospital-based and realise them as a small portable devices, facilitating mobile and remote healthcare strategies
All illness management may benefit from Artificial Intelligence (AI); respiratory disease management is an enduring key medical theme which encompasses Asthma, chronic obstructive pulmonary disease (COPD), pulmonary hypertension, bronchiectasis, sleep apnoea, etc. [7] and attributes to around 20% of UK deaths [8]
A Decision Support Systems (DSS) may be produced from domain knowledge to inform, prompt, and advise respiratory disease suffers in a tailored fashion
Summary
As medical technology gets smaller, smarter, and more able to process large volumes of data, there are opportunities to take devices which are hospital-based and realise them as a small portable devices, facilitating mobile and remote healthcare strategies. Supervised machine learning may be used to build a classification model which may be used to indicate when COPD symptoms are likely to deteriorate This model may be produced from an annotated dataset leveraging environmental metrics, biomarkers, an individual’s medical history and current state. A DSS may be produced from domain knowledge to inform, prompt, and advise respiratory disease suffers in a tailored fashion This DSS may take inputs from PEF measurements, environmental data, and supervised machine learning classification of an individual’s parameters. The realisation of such a solution will be the subject of future work and will be informed by the quality and quantity of data that can be reliably obtained from sufferers of respiratory diseases. It is a universal method which creates a usable examination which may act as a starting point to tackle the challenges
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