We need to chat about artificial intelligence With the arrival of large language models such as ChatGPT, AI is reshaping how we work and interact Long foretold and often dismissed, artificial intelligence (AI) is now reshaping how we work and interact as a society.1 For every claim that AI is overhyped and underperforming, only weeks or
Challenges for Evaluating AI and Practical Implications Artificial intelligence (AI) promises to transform clinical decision-making processes as it has the potential to harness the vast amounts of genomic, biomarker, and phenotype data that is being generated across the health system including from health records and delivery systems, to improve the safety and quality of care
Can co-designing artificial intelligence tools with general practitioners deliver better patient outcomes and what impact will it have on Doctors? And what about the healthcare system? We took it to the test in a study with general practitioners simulating an AI documentation assistant for use in patient consultations. While artificial intelligence is advancing rapidly across
Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical
Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical
Objective: To create practical recommendations for the curation of routinely collected health data and artificial intelligence (AI) in primary care with a focus on ensuring their ethical use. Methods: We defined data curation as the process of management of data throughout its lifecycle to ensure it can be used into the future. We used a
Background: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions. Objective: To form consensus about perceptions,
Objectives : To summarise the state of the art during the year 2018 in consumer health informatics and education, with a special emphasis on the special topic of the International Medical Informatics Association (IMIA) Yearbook for 2019: “Artificial intelligence in health: new opportunities, challenges, and practical implications”. Methods : We conducted a systematic search of
Objective: Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health. Methods: A search
Apply now The Australian Institute of Health Innovation at Macquarie University are seeking suitably qualified candidates with pioneering ideas for research into understanding the current health system response to the pandemic and strategies for future crisis preparedness. They have FIVE scholarships available with the Ph.D. topic to be determined taking into account the interests, experience, and prior
Objective The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Materials and Methods Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding
Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical
OBJECTIVE: Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. METHODS: We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational
Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital
Artificial Intelligence (AI) is often proclaimed as an approach to augment or substitute for the limited processing power of the human brain of primary health care professionals. However, there are concerns that AI mediated decisions may be hard to validate and challenge, or may result in automation biases, even rouge decisions. While the use of
