Modern healthcare systems are associated with expansion of costs, complexities, the growing number of treatment options as well as growing streams of information and this hinders the medical practitioners from reaching an optimal treatment option and decision. Artificial intelligence which is non-disease specific comes in to resolve these issues by assisting in clinical environment simulation and also offering basic clinical services where it acts and think like a doctor.
Artificial intelligence combines the Markov decision process and the dynamic decision networks to learn from the provided clinical data and then aid in developing complex plans through simulation of alternative decision routes. Through the use of the artificial intelligence, it is possible to capture conflicting and synergistic interaction components of the healthcare systems. Artificial intelligence, through maintaining belief states of a patient’s health status and functions, can effectively operate in a partially observable environment and enable planning and re-planning of actions to be performed even as more observations are made.
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Research on artificial intelligence clearly indicates its feasibility in that it has proved to outperform the current methods used in fee-for-service treatment services offered in the healthcare facilities today. Through the use of artificial intelligence, there is a relative cost reduction and low costs are considered optimal when it comes to service delivery (Bennett & Hauser, 2012). Patient outcome also increases with the application of the artificial intelligence in the health care systems. Advancement of the artificial intelligence model parameters is expected to further improve patient outcome while reducing the related costs in a drastic manner, even to half of the current cost in the fee-for-service setups. With careful design and problem development, artificial intelligence simulation framework is known to help in optimal decision making even when the conditions are complex and uncertain. Further improvements in the AI field and through the use of machine learning algorithms, it will be possible to have personalized medicine and health care services while on the move.
Artificial intelligence aims at mimicking human cognitive capabilities. Today, I have observed that artificial intelligence has brought in a paradigm shift in the healthcare sector one that is associated with the increase in the availability of clinical data and information and its seamless access. AI has also brought in a rapid progress in the medical analytics techniques. One of the benefits of artificial intelligence application in the medical field is its application in all types of data, whether structured or unstructured which in turn aids doctors in not only making ideal decisions abut also making them in the least time possible.
I think that some diseases and conditions become fatal when they are either not detected or managed in time. Cancer, for instance, which refers to development and growth of abnormal cells, is treatable but also extremely fatal at the same time. Early detection through screening is effective in the treatment and the management of cancer. X-ray machines, that are introduced as a result of the advancement of artificial intelligence has aided in the diagnosis of any type of cancer and this in turn assists in its early identification and timely provision of the possible treatment. In some cases, after an x-ray image is captured, doctors are known to disagree about the extent to which the cancer has developed and this may lead to a slower intervention (Walsh, 2020). Artificial intelligence, on the other hand, critically considers every detail in the image, and offers a detailed diagnosis report of the cancer even on aspects that would not be visible with a naked eye hence it is better than human experts.
References
Bennett, C. C., & Hauser, K. (2012). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Retrieved from https://www.sciencedirect.com/science/article/pii/S0933365712001510
Walsh, F. (2020). AI 'outperforms' doctors diagnosing breast cancer. Retrieved from https://www.bbc.com/news/health-50857759
