Rapidly advancing artificial intelligence technologies are gradually changing health care practices and bring a paradigm shift to the medical system. While increasing the availability of data and enhancing analytics techniques, they facilitate practices that were previously considered to be an area only for human experts. Although there are several challenges such as subjectivity of results, possible security, and privacy problems, and presumable unethicality of AI systems, further development of technologies will eliminate the concerns.
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It is often thought that using AI systems in health care leads to the subjectivity of results, as while learning, AI tends to copy human prejudices regarding decision-making. Although doctors should always be cautious and check whether there are any glaring mistakes due to misuse of AI, the technology is quickly advancing as it receives larger amounts of input data to learn on. What is essential is that the more data the AI gets, the fewer percent of prejudiced information it receives. The main aim of AI technologies in the health care system is “to assist clinical decision-making” through uncovering relevant information from data (He, Baxter, Xu, Xu, Zhou & Zhang, 2019, p. 30). As He et al. (2019) argue, with AI, it is easier to generate a diagnosis, select therapy, reduce medical errors, manage risk predictions, and improve productivity. The health system will reap enormous benefits of using AI, so there is a tendency of developing countries sharing more and more medical data for the development and learning of AI, so the chance of it being biased decreases.
Some people are afraid that the application of AI in health care will pose a threat to the privacy and security of patients’ data. However, data leaks are not so often in the medical sphere, and scientists continue to develop new strategies to preserve and secure data. As Yu, Beam & Kohane (2018) state, “privacy-preserving methods can permit secure data sharing through cloud services (such as third-party-hosted computing environments),” and advancing blockchain may be a solution (p. 727). To break into a blockchain system, more than 51% of its nodes have to be hacked at the same time, and the new block has to be inserted into each of them. Consequently, blockchain systems are secure as binary computers do not have enough computing power to hack them, and quantum computers are far from reaching the level of being able to do so.
Finally, the third counter-argument to implementing the AI in health care systems is that it is unethical and presents an ethical issue associated with patients’ choices. It is considered that some medical workers may have a false sense of security while using AI systems in research or analysis. However, AI does provide incredibly precise results while conducting a study, and with time passing and technology developing, it becomes an equally reliable source of information as a human is. Jiang et al. (2017) state that “in a cancer research, 99% of the treatment recommendations from Watson (AI system) are coherent with the physician decisions” (p. 241). Another concern is that there is a lack of legal liability regarding possible medical errors, but it is easily fixed by appointing people responsible for specific areas of AI performance. Troubles of reaching consent between machines and humans will also be eliminated in the future since technology is developing to be able to address issues conclusively.
AI-based technologies benefit the health care system today, and their influence will continue to grow in the future, as they facilitate research and analysis, spur productivity, and assist clinical decision-making. Although there are some challenges regarding their implementation, such as subjectivity of results, the possible threat of data hacks, and the unethicality of AI systems, all of them can be overcome by advancing technologies. Vast amounts of data and advanced algorithms may help reduce subjectivity; blockchain may allow secure data, and developing technologies will boost the credibility of AI research and analysis.
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X. & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. Web.
Jiang F., Jiang Y., Zhi, H., Dong, Y., Li, H., Ma, S., … Wang, Y. (2017) Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2: e000101, 230-243. Web.
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Yu, K.-H., Beam, A. L. & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. Web.