In recent years, the number of artificial intelligence startups has rapidly increased to a total of 106 (CB Insights Research, 2017), indicating that the role of AI will gain importance in healthcare . AI has especially made a lot of progress in the medical imaging industry. The number of image scan on human body parts has skyrocketed, while the number of radiologists has not significantly increased in the last years (Steve O’Hear, 2017).
Artificial intelligence aims to solve this problem by processing the imaging data in the cloud, using deep learning algorithms which can identify a patient’s potential medical issues, aiding the doctor in patient diagnosis (Fornell, 2017). AI system is able to perform these human tasks by ‘studying’ an excessive database of patient cases. In this way, its deep learning algorithms allow the system to recognize body part anomalies. Study has proven that if an AI system has been given sufficient amounts of data, it can outperform human diagnosis (Fornell, 2017). However, the use of AI does not imply the end of physicians. It simply means that the workflow of physicians changes as they are needed to verify the diagnosis and to devise corresponding treatment plans. This results in increased face-to-face time with patients, and helping more patients in less time. AI will enable cost cuttings up to 50% while improving performance outcomes by 30 to 40% (Frost & Sullivan, 2017).
To analyze how AI is disrupting the medical imaging industry, Porter’s Five Forces model can be applied. Barriers to entry are lower, as the industry is accessible to all who have expertise in AI software. Due to dependency on medical imaging hardware, the bargaining power of buyers, in this case hospitals and clinics, remains unchanged. Bargaining power of suppliers however does increase as AI systems require millions of images to learn before its performance reaches acceptable levels. This data, is usually owned by traditional suppliers such as Philips. Threat of substitutes diminish as medical imaging techniques are becoming more accessible, making the development of other techniques less interesting. As core IT businesses, such as IBM, are applying their AI technologies in healthcare, traditional players will experience increasing industry rivalry.
By 2021, Accenture (2017) expects the AI health market to be worth $6.6 billion which means that its yearly compound growth rate equals 40%. These market predictions demonstrate the continuous investment in AI healthcare technology, which can eventually be integrated in everyday life. If done so, healthcare becomes accessible to anyone, anywhere and anytime. An example of accessible healthcare is the portable 3D-ultrasound device Butterfly Network Inc. is developing. The device creates 3D images of human body parts and sends this data in real-time to the cloud, where it is analyzed and potential anomalies are identified. This device will make medical imaging more accessible, as it allows anyone, anywhere and anytime to create medical images without being dependent on hospital equipment.
In conclusion, the future of AI healthcare is a promising one, but research and development is still required to optimize the technology for everyday use. As Bill Gates assured us “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”
-Group 16
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References
Accenture. (2017). Artificial Intelligence in Healthcare | Accenture. [online] Available at: https://www.accenture.com/us-en/insight-artificial-intelligence-healthcare [Accessed 20 Sep. 2017].
CB Insights Research. (2017). From Virtual Nurses To Drug Discovery: 106 Artificial Intelligence Startups In Healthcare. [online] Available at: https://www.cbinsights.com/research/artificial-intelligence-startups-healthcare/ [Accessed 20 Sep. 2017].
Fornell, D. (2017). How Artificial Intelligence Will Change Medical Imaging. [online] Imaging Technology News. Available at: https://www.itnonline.com/article/how-artificial-intelligence-will-change-medical-imaging [Accessed 19 Sep. 2017].
Frost & Sullivan. (2017). From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare. [online] Available at: https://ww2.frost.com/news/press-releases/600-m-6-billion-artificial-intelligence-systems-poised-dramatic-market-expansion-healthcare [Accessed 20 Sep. 2017].
O’Hear, S. (2017). AIDoc Medical raises $7M to bring AI to medical imaging analysis. [online] TechCrunch. Available at: https://techcrunch.com/2017/04/26/aidoc-medical/ [Accessed 20 Sep. 2017]
