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As artificial intelligence (AI) advances, more industries, including healthcare, are investigating how to utilize it best to increase performance and outcomes. As the requirements of an aging global population going to remain to pressure on already worn-out systems, the health sector stands to gain significantly (Bhardwaj et al., 2017). Automated healthcare procedures created by medical AI and machine learning (ML) have the potential to substantially improve efficiency, lower costs, and improve the quality of care and mortality rates. In order to ensure that this new technology is adequately incorporated into morally upstanding, patient-centered care applications, discussions addressing the applicability and deployments of medical AI and ML must take place.
Predictive analytics is a technique used in medicine to analyze patient information and predict the likelihood of specific diseases. Recent research has demonstrated that AI can discover or diagnose ailments that are typically challenging to do so, such as genetic disorders and neurodegenerative diseases (Jiang et al., 2017). Machine learning, a branch of AI that enables computers to learn and advance without requiring human input, allows AI algorithms to develop constantly. Since the algorithms modify according to the data they have been introduced to, much like behavior modification, knowledge is achievable when the algorithms are subjected to more data.
The identification of side effects and therapeutic issues is improved by machine learning. It is challenging to complete using traditional means because some medications and therapies are effective in some patient populations but not in others. ML can assist in the analysis of side effect data, the development of insights, contexts, and models that can help forecast better results, and the assistance of GPs in determining the best course of therapy for the disease (Nithya & Ilango, 2017). On the basis of medical history and existing problems, it aids in the prediction of health hazards, such as the possibility of dying after surgery. Additionally, it can suggest that individuals who have illnesses like diabetes would need to stay in the hospital.
Health informatics is a challenging field, and machine learning is the area of computer science that is growing the fastest. The purpose of machine learning is to create predictive algorithms that allow and improve over time (Bhardwaj et al., 2017). The secondary healthcare industry has greatly benefited from machine learning prediction systems, which are widely employed in many other disciplines. The healthcare sector is facing difficulties in crucial areas like record management administration and computer-aided diagnosis and disease projections due to the need to lower healthcare costs and the shift to individualized treatment.
The best candidates for AI integration are diseases with variable patient prognoses depending on when they are discovered along the disease progression timeline. This is particularly important for illnesses that doctors struggle to diagnose confidently because they lack crucial information (Jiang et al., 2017). When detected early, developmental dysplasia of the hip, for instance, has a considerably better prognosis; nevertheless, there are frequently little to no complaints in the early stages. Furthermore, a predictive model that can take into account pertinent information and properly determine an otherwise challenging diagnosis could significantly affect the lives of many people.
To summarize, it is incredible that AI and ML might turn a largely reactive organization into a proactive one when contemplating its possibilities in the healthcare industry. Better patient outcomes, fewer human errors, increased efficiency, and cheaper healthcare costs could result from this. In order to carefully manage the explosive expansion of AI, especially in healthcare, strict limits must be set. Medical AI and ML applications and implementations must be discussed in order to ensure that this novel technology is effectively incorporated into patient-centered, morally responsible healthcare treatments.
References
Bhardwaj, R., Nambiar, A. R., & Dutta, D. (2017). A study of machine learning in healthcare. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) 2, IEEE. 236-241. 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(4). Web.
Nithya, B., & Ilango, V. (2017). Predictive analytics in health care using machine learning tools and techniques. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE. 492-499. Web.
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