With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine [...]
Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better understand the consequences of implementing this technology.
We convened a national citizens’ jury to discuss the question: “Under what circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?”. The aim of deliberative democratic methods, developed in political science and government, is to enhance democracy [...]
A recent study showed that a moderately sized systematic review (index SR) could be completed in 2 weeks (2weekSR) by protecting time to solely focus on SR tasks and supported by systematic review automation (SRA) tools. Given that SRs are time and resource intensive that [...]
Most health information does not meet the health literacy needs of our communities. Writing health information in plain language is time-consuming but the release of tools like ChatGPT may make it easier to produce reliable plain language health information. The objective of this study is [...]
The objective of this study is to evaluate an approach using automation and crowdsourcing to identify and classify randomized controlled trials (RCTs) for rheumatoid arthritis (RA) in a living systematic review (LSR). Records from a database search for RCTs in RA were screened first by [...]
The purpose of this study is to summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.
Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare [...]
Although women were positive about the potential of breast screening AI, they argued strongly that humans must remain as central actors in breast screening systems and consistently expressed high expectations of the performance of breast screening AI. Women expected clear lines of responsibility for decision-making, [...]
As new evidence emerges from prospective trials of AI for breast screening, population screening programmes and imaging services there will also be a need to consider participants’ views and expectations of AI performance before it can be implemented in screening. Maintaining public trust in cancer [...]