AI in Health: State of the Art, Challenges, and Future Directions

By Fei Wang, Anita M. Preininger. Yearbook of Medical Informatics, 2019

320 citations recorded by the source corpus.

Summary Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/dfa67fa1b5eafc1e350c4a7357ce6c57aa6b989a

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Citations in the corpus

Interpretability of machine learning‐based prediction models in healthcare (2020, background): “For example, in a recent review paper on the state of AI in healthcare Wang, and Preininger (2019) quote Geoff Hinton, one of the pioneers in the field of AI, saying: “Policymakers should not insist on ensuring people understand exactly how a given AI algorithm works, because people can’t explain…”
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “Today, AI in healthcare encompasses a broad range of applications [7].”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, background): “This might for example entail an initial risk assessment of vulnerabilities in data and software, including the risk of re-identi ﬁ cation 33 , the risk of data loss and manipulation 33,35 , and the risk of adversarial attacks 9,22,23,26,35,43,59 .”
Precision Medicine, AI, and the Future of Personalized Health Care (2020, methodology): “5 For example, the difference among institutions in coding definitions, report formats, or cohort diversity, may result in a model trained using one-site data to not work well in another site (https://www.bmj.com/conte nt /368/bmj. l6927).”
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records (2021, background): “With the accumulation and better availability of electronic health records (EHR) [1], [2], health analytics becomes one of the most important frontiers for data mining and artificial intelligence [3].”
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, background): “35 One popular criticism of AI technologies is the ‘black-box’ problem.”