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AIM and Business Models of Healthcare

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Artificial Intelligence in Medicine

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Abstract

Artificial intelligence (AI) and machine learning in healthcare are growing at an unprecedented rate. Myriad uses of medical AI, ranging from tumor identification on imaging to workforce management, make use of a wealth of available healthcare data. These models are becoming increasingly commercially available. However, much of the utility of medical AI depends on the quality of the data models trained on, and critically, the contexts and biases within which these models are created. In this chapter, we first describe a business-informed framework that influences product development and commercialization of these technologies. We describe the consumer side that includes purchasers, end users, and patients. Subsequently, we underscore the pitfalls of the assumption that models trained in one context can be applied to another, that is, the myth of generalizability. We propose solutions to these problems and describe the importance of co-creation and multi-stakeholder engagement in designing medical AI. We highlight the need to define value metrics that consider equity and the mitigation of healthcare disparities. Lastly, we draw attention to open ethical, legal, and policy questions that must be answered as the role of AI in medicine progresses and grows.

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Author information

Authors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Edward Christopher Dee

  2. Harvard Business School, Cambridge, MA, USA

    Ryan Carl Yu

  3. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leo Anthony Celi

  4. Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Leo Anthony Celi

  5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Leo Anthony Celi

  6. MIT Sloan School of Management, Cambridge, MA, USA

    Umbereen Sultana Nehal

  7. University of Massachusetts Medical School, Worcester, MA, USA

    Umbereen Sultana Nehal

  8. Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leo Anthony Celi

Authors
  1. Edward Christopher Dee
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  2. Ryan Carl Yu
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  3. Leo Anthony Celi
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  4. Umbereen Sultana Nehal
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Corresponding author

Correspondence to Leo Anthony Celi .

Editor information

Editors and Affiliations

  1. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

    Niklas Lidströmer

  2. Imperial College London, London, UK

    Hutan Ashrafian

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Dee, E.C., Yu, R.C., Celi, L.A., Nehal, U.S. (2022). AIM and Business Models of Healthcare. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-64573-1_247

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  • DOI: https://doi.org/10.1007/978-3-030-64573-1_247

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64572-4

  • Online ISBN: 978-3-030-64573-1

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