This paper presents an analytical framework for conducting academic reviews in the field of Healthcare Systems Engineering, employing ChatGPT, a state-of-the-art tool among recent language models. We utilized 9,809 abstract paragraphs from conference presentations to systematically review the field. The framework comprises distinct analytical processes, each employing tailored prompts and the systematic use of the ChatGPT API. Through this framework, we organized the target field into 11 topic categories and conducted a comprehensive analysis covering quantitative yearly trends and detailed sub-categories. This effort explores the potential for leveraging ChatGPT to alleviate the burden of academic reviews. Furthermore, it provides valuable insights into the dynamic landscape of Healthcare Systems Engineering research.

We have a great opportunity to get smarter about the way we are using AI and machine learning with datasets to improve the quality of clinical care" - Simon Stevens, chief executive of NHS England The adoption of artificial intelligence in healthcare is on the rise and is solving a variety of problems for patients, hospitals and the healthcare industry overall. Sir John Bell's Life Sciences Industrial strategy identified the faster application of Artificial Intelligence (AI) as a priority and the NHS England is to invest more in AI over the next 12 months and create new Digital Innovation Hubs. These new Digital Innovation Hubs will enable researchers to engage with a meaningful dataset. AI is increasingly being applied in healthcare and medicine, with the greatest impact being achieved thus far in medical imaging. A recent Lancet editorial entitled'Augmenting diagnostic vision with AI' suggested artificial intelligence had the potential to interpret clinical data more accurately and more rapidly than medical specialists', such as radiologist and dermatologist who analyse hundreds of thousands of images over their career.