Why AI in healthcare needs human-centered design

Of course, physician perceptions of AI will continue to evolve as its capabilities mature. Just like a junior resident that first joins a team, AI needs to demonstrate its added value and earn trust over time.

With AI as a trusted collaborator that is seamlessly embedded into the workflow, radiologists will be freed from mundane tasks and able to spend more time on complex cases, increasing their impact on clinical decision-making. Healthcare professionals in other areas of medicine will increasingly benefit from AI assistance, too - whether it's in digital pathology, acute care, image-guided therapy, or chronic disease management. I firmly believe it will make their work more valuable and rewarding, not less.

But as I have hoped to show, that future won't happen by itself. It will only happen by design.

Designers must take an active role from the very start of AI development, working alongside data scientists, engineers, and clinical experts to create AI-enabled experiences that make a positive difference to healthcare professionals and patients alike.

Only then will we see AI in healthcare fully deliver on its promise; if we put people, not technology, first.

Note
[i] e/MTIC or Eindhoven MedTech Innovation Center is a research collaboration between Eindhoven University of Technology, Catharina Hospital, Maxima Medical Center, Kempenhaeghe Epilepsy and Sleep Center, and Philips.

References
[1] Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, and Dominic King. 2019. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine 17, 1: 195. https://doi.org/10.1186/s12916-019-1426-2

[2] Lea Strohm, Charisma Hehakaya, Erik R. Ranschaert, Wouter P. C. Boon, and Ellen H. M. Moors. 2020. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. European Radiology. https://doi.org/10.1007/s00330-020-06946-y

[3] Atul Gawande. 2018. Why doctors hate their computers. New Yorker. https://www.newyorker.com/magazine/2018/11/12/why-doctors-hate-their-computers

[4] Robert M. Wachter. 2015. The digital doctor: hope, hype, and harm at the dawn of medicine's computer age.

[5] D. W. De Boo, M. Prokop, M. Uffmann, B. van Ginneken, and C. M. Schaefer-Prokop. 2009. Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs. European Journal of Radiology 72, 2: 218-225. https://doi.org/10.1016/j.ejrad.2009.05.062

[6] Robert M. Nishikawa, Alexandra Edwards, Robert A. Schmidt, John Papaioannou, and Michael N. Linver. 2006. Can radiologists recognize that a computer has identified cancers that they have overlooked? In Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 614601. https://doi.org/10.1117/12.656351

[7] W. Jorritsma, F. Cnossen, and P. M. A. van Ooijen. 2015. Improving the radiologist-CAD interaction: designing for appropriate trust. Clinical Radiology 70, 2: 115-122. https://doi.org/10.1016/j.crad.2014.09.017

[8] John D. Lee and Katrina A. See. 2004. Trust in automation: designing for appropriate reliance. Human Factors 46, 1: 50-80. https://doi.org/10.1518/hfes.46.1.50_30392

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