. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095.

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Affiliations

• 1 Microsoft Research, Cambridge, UK.
• 2 University College London, London, UK and director, NIHR UCLH Biomedical Research Centre, London, UK.

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Junaid Bajwa et al. Future Healthc J. 2021 Jul.

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. 2021 Jul;8(2):e188-e194. doi: 10.7861/fhj.2021-0095.

Affiliations

• 1 Microsoft Research, Cambridge, UK.
• 2 University College London, London, UK and director, NIHR UCLH Biomedical Research Centre, London, UK.

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Abstract

Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.

Keywords: AI; digital health.

© Royal College of Physicians 2021. All rights reserved.

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Figures

Fig 1.

Multi-step, iterative approach to build…

Fig 1.

Multi-step, iterative approach to build effective and reliable AI-augmented systems in healthcare.

Fig 1.

Multi-step, iterative approach to build effective and reliable AI-augmented systems in healthcare.

Fig 2.

Potential applications for the InnerEye…

Fig 2.

Potential applications for the InnerEye deep learning toolkit include quantitative radiology for monitoring…

Fig 2.

Potential applications for the InnerEye deep learning toolkit include quantitative radiology for monitoring tumour progression, planning for surgery and radiotherapy planning.

Fig 3.

An overview of the main…

Fig 3.

An overview of the main neural network model architecture for AlphaFold. MSA =…

Fig 3.

An overview of the main neural network model architecture for AlphaFold. MSA = multiple sequence alignment.

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