Abstract
Artificial intelligence (AI)-augmented medical device technology provides an important opportunity to optimize the detection, diagnosis, and treatment outcome for individual patients. By combining AI-augmented medical devices with closed loop feedback from continuous sensor measurements performed on the patient, AI-augmented Medtech has the potential to deliver truly personalized patient treatment. This chapter provides an overview of common AI definitions and highlights current misconceptions of artificial intelligence. It describes ongoing efforts by regulators to develop a regulatory framework for medical software and discusses a classification for AI learning schemes. Usage examples providing added patient value, in observation, visual analysis, diagnostics, or treatment, are provided. Data sources to train AI are discussed as well as data quality, a critical prerequisite for the development of safe and effective medical devices augmented by AI. The ethical aspects of AI in healthcare are presented as well as the changes and adaptations envisaged for the medical profession and physician education. The chapter concludes that the field of personalized AI-augmented Medtech will be one of the core applications for AI in healthcare and is expected to grow continuously over the next years, despite potential challenges such as product liability and data privacy regulations.
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Notes
- 1.
In The Hitchhiker’s Guide to the Galaxy, published by Douglas Adams in 1979, 42 was the answer to the ultimate question of life, the universe, and everything, calculated by a supercomputer named Deep Thought over a period of 7.5 million years.
- 2.
The benefit can be an improved treatment, safety, or outcome, and/or an increased efficiency of utilization of resources, and/or a measurable relief of personnel resources.
- 3.
One frequently observes AI applications which only provide a command-line interface (CLI) and that rely on manual integration in every clinical pathway or remain in the state of an academic result.
- 4.
AI as a data-driven approach does not necessarily need to be the right option for every digitalization initiative.
- 5.
This is where the added value is being realized. Understanding the context and perspective of both the clinical problem and the application is critical for obtaining the benefit, hence providing value to the user/patient. Not involving user/patient and healthcare provider, hence not fully understanding the clinical performance objectives and risks, is a major factor of failing AI projects in medicine.
- 6.
FDA Re: K191370, Trade/Device Name: DreaMed Advisor Pro, Insulin therapy adjustment device, Regulatory class: Class II, Product code: QCC.
- 7.
FDA Re: K211222, Trade/Device Name: qER-Quant, Medical image management and processing systems, Regulatory Class: Class II, Product Code: QIH
- 8.
FDA Re: K190898, Trade/Device Name: Sight OLO, Regulation Number: 21 CFR 864.5220, Regulation Name: Automated Differential Cell Counter, Regulatory Class: Class II, Product Code: GKZ.
- 9.
FDA—Premarket Approval Application (PMA) Number: P160017/S076Device Trade Name: MiniMed 770G System, Device Procode: OZP, Medtronic MiniMed, Inc.
- 10.
P9_TA (2020)0276 Civil liability regime for artificial intelligence. European Parliament resolution of October 20, 2020, with recommendations to the Commission on a civil liability regime for artificial intelligence (2020/2014(INL)).
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Neumann, D., Schlichting, S., Rackebrandt, K., Klasen, E. (2023). Artificial Intelligence Augmented Medtech: Toward Personalized Patient Treatment. In: Cesario, A., D'Oria, M., Auffray, C., Scambia, G. (eds) Personalized Medicine Meets Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-031-32614-1_14
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