The last few years, the art of artificial intelligence (from now on referred to as AI) has made an expansion to the wider audience. AI is the phenomenon to make technologies work together to sense, comprehend, act, and learn at human-like intelligence levels (Accenture, z.d.). AI, in special deep learning, is upcoming in the medical world and especially in the process of image-recognition. Deep learning is also becoming a major tool in the drug discovery, patient monitoring, medical diagnostics, hospital managing and so on (Hosny et al., 2018). Deep learning contains the study of computational models, composed of multiple processing layers, to learn representations of data with multiple levels of abstraction (LeCun et al., 2015).
In the past, trained doctors evaluated the medical images themselves to detect diseases. AI has created the possibility to automatically detect complex patterns in the imaging data. An advantage of AI in comparison to manually evaluating the images is that it gives quantitative assessments and is objective instead of subjective (Hosny et al., 2018).
A second advantage of AI in the medical field is the efficiency of radiological imaging. The data that needs to be analyzed is still growing at an immense pace in comparison to the radiologists available to evaluate the images. Thus, the workload of the radiologists has
increased significantly. In some clinics, the workload has increased to such a level that the radiologists need to evaluate an image every 3 or 4 seconds for eight hours long in order to meet the demands. These long working days and the high workload only increase the chance of human error. AI can bring efficiency into the imaging world because it is automated, only takes a couple seconds per image, and is not sensitive for human error (Hosney et al., 2018).
An example of the use of AI in the medical field is thoracic imaging. Thoracic imaging includes the search for lung cancer, which is the most common and deadly form of cancer. AI can help to detect the cancer early on and save many lives by automatically identifying the nodules and detect whether they are benign or malignant (Hosney et al., 2018).
On the other hand, there are also disadvantages of implementing AI in the medical field. The first challenge is data availability. For AI, large sets of data are needed for successful implementation. Within the healthcare industry, the clinics and hospitals are not keen on sharing their data with other clinics and hospitals. As a result, the separate clinics do not have a lot of data. Another challenge is the difficulty of implementation and the lack of understanding of AI. AI is a difficult to understand and implement for doctors in the health care industry because they usually do not have a background in the technological world of AI (Aung et al., 2021).
To conclude, doctors remain vital elements in the healthcare industry. AI could definitely help increase the effectiveness of the industry and relieve the employers of a part of their workload. However, AI has too many negative sides to it to really call it disruptive.
References:
Accenture. (n.d.). What is artificial intelligence? Retrieved October 4, 2021, from https://www.accenture.com/us-en/insights/artificial-intelligence-summary-index
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nat Rev Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.https://doi.org/10.1038/nature14539
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021). The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4–15. https://doi.org/10.1093/bmb/ldab016
