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Currently, the healthcare industry faces significant challenges; therefore, technology should be applied to deploy more efficient, precise, and impactful interventions during care delivery. Hamet & Tremblay (2017) explain that patients demand more from healthcare professionals with the evolvement of pay structures and increasing data volumes. Therefore, artificial intelligence (AI) acts as the engine that enhances healthcare settings (Yu et al., 2018). Although machine learning and AI collaboration will transform a wide range of areas such as cloud database, decision-making process, medical processes, mental healthcare radiology, and telehealth, there are challenges associated with its adoption.
The documentation of health records has been hectic because of paperwork. However, Lisowski (2019) explains that it is possible to convert them into electronic documents using natural voice processing and computer vision. Hamet & Tremblay (2017) state that AI transformation will affect mHealth apps and enable personalization of data. As a result, patients can access all health information such as bills, insurance plans, and prescriptions. Moreover, individuals will get essential health records quickly through mobile phones. Additionally, patients will be advised on healthier lifestyle changes considering their particular circumstances (Yu et al., 2018). The decision-making process in healthcare is different and more complicated than in other areas. Reddy et al. (2019) explain that AI can help design patient-friendly platforms that combine professional expertise and personal needs to enhance their relationship and participation during treatment. As a result, every individual will become a valuable partner while conversing with healthcare professionals.
Mental Health Care is among the areas that will significantly benefit from AI. The reason is that artificial intelligence will allow quick tracking of valuable data from sets with massive medical records of the patients (Lisowski, 2019). In addition, the improved AI tools will assist in choosing the best method of treatment and predict the likely results of specific solutions. AI will also help fight depression, the cases of which have rapidly increased over time, by implementing AI into data analysis and considering features such as gender, age, genetics, or the environment. This will encourage doctors to develop the optimal treatment earlier than it is done currently.
The AI application in telehealth can allow patients to video chat or exchange messages to have their conditions diagnosed and receive advice on the best treatment methods and prescriptions. As a result, individuals in areas where specialists are not available and the elderly with commuting challenges will benefit. Machine learning (ML) will play a crucial role in health institutions specialized in radiology (Hamet & Tremblay, 2017). ML will support steps like imaging examination, initial scheduling, or final diagnosis stating. Therefore, ML tools will assist in creating medical reports and treatment plans.
Challenges to the Adoption of AI in Healthcare
Although AI is gaining more popularity in the world, many healthcare organizations are hesitant to implement it. As Shaw et al. (2018) explain, healthcare professionals fear that the innovations will take the jobs previously done by human beings. Additionally, healthcare providers lack sufficient knowledge of implementing AI in their practice. Moreover, the relatively low number of experts with AI technical know-how and good data scientists makes it challenging to adopt it (Shaw et al., 2018). Individuals believe that AI can be biased because it only depends on the available data to decide. Therefore, if the collected information is incorrect, it increases the possibility of making wrong decisions and conclusions.
References
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine.Metabolism, 69, 36-40. Web.
Lisowski, E. (2019). AI will revolutionize healthcare. Medium. Web
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery.Journal of the Royal Society of Medicine, 112(1), 22-28. Web.
Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenges. Journal of Medical Internet research, 21(7), 13659. Web.
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare.Nature biomedical engineering, 2(10), 719-731. Web.
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