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 [...]
Applications of artificial intelligence (AI) have the potential to improve aspects of healthcare. However, studies have shown that healthcare AI algorithms also have the potential to perpetuate existing inequities in healthcare, performing less effectively for marginalised populations. Studies on public attitudes towards AI outside of [...]
Artificial intelligence (AI) has been proposed to reduce false-positive screens, increase cancer detection rates (CDRs), and address resourcing challenges faced by breast screening programs. We compared the accuracy of AI versus radiologists in real-world population breast cancer screening, and estimated potential impacts on CDR, recall [...]
There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race). Our objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to [...]