CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 174-178
DOI: 10.1055/s-0039-1677935
Section 7: Consumer Health Informatics and Education
Georg Thieme Verlag KG Stuttgart

Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications

Findings from the Yearbook 2019 Section on Education and Consumer Health Informatics
Annie Y. S. Lau
1  Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Australia
Pascal Staccini
2  IRIS Department, URE RETINES, Faculté de Médecine, Université Côte d'Azur, France
Section Editors for the IMIA Yearbook Section on Education and Consumer Health Informatics› Institutsangaben
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16. August 2019 (online)



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 articles published in PubMed using a predefined set of queries that identified 99 potential articles for review. These articles were screened according to topic relevance and 14 were selected for consideration as best paper candidates. The 14 papers were then presented to a panel of international experts for full paper review and scoring. Three papers that received the highest score were discussed in a consensus meeting and were agreed upon as best papers on artificial intelligence in health for patients and consumers in the year 2018.

Results: Only a small number of 2018 papers reported Artificial Intelligence (AI) research for patients and consumers. No studies were found on AI applications designed specifically for patients or consumers, nor were there studies that elicited patient and consumer input on AI. Currently, the most common use of AI for patients and consumers lies in secondary analysis of social media data (e.g., online discussion forums). In particular, the three best papers shared a common methodology of using data-driven algorithms (such as text mining, topic modelling, Latent Dirichlet allocation modelling), combined with insight-led approaches (e.g., visualisation, qualitative analysis and manual review), to uncover patient and consumer experiences of health and illness in online communities.

Conclusions: While discussion remains active on how AI could 'revolutionise' healthcare delivery, there is a lack of direction and evidence on how AI could actually benefit patients and consumers. Perhaps instead of primarily focusing on data and algorithms, researchers should engage with patients and consumers early in the AI research agenda to ensure we are indeed asking the right questions, and that important use cases and critical contexts are identified together with patients and consumers. Without a clear understanding on why patients and consumers need AI in the first place, or how AI could support individuals with their healthcare needs, it is difficult to imagine the kinds of AI applications that would have meaningful and sustainable impact on individual daily lives.