**Read and provide a response to the following reports (summarize the findings and include your own insights/thoughts) initial post should be 600 words ( read attached article and follow directions)
**For your replies, respond to the two classmates, identifying at least 1 strength and 1 weakness in each classmate’s reasoning.( replies should be 455 words each)
apa format with biblical and scholarly references should be the format- atleast 3 sources are required
classmate #2 is not finished yet but the post will be posted by Tuesday but it still included in this question and will need to be answered by Friday morning as well
A Markov decision process relies on the notions of state, describing the current situation of the agent, action (or decision), affecting the dynamics of the process, and reward, observed for each transition between states. Such a process describes the probability of triggering a transition to state so and receiving a certain reward r when taking decision, a in state” (Sigaud & Buffet, 2010, p3-4).
With the continued growth of healthcare cost in the U.S, numerous innovations and cost cutting processes are being considered and implemented. Information technology as a means of improving patient care has been widely utilized. Electronic medical records, patient portals, digitized medical devices, and even wearables are becoming more broadly used. Artificial intelligence (AI) systems is another realm that are capable of machine learning that goes beyond traditional medical transactions and record-keeping to analyze data, make decisions and exercise better judgement.
Lamanna & Byrne (2018) indicated that cognitive technologies are being been introduced in health care in part to reduce human decision-making and the potential for human error in providing care. Medical errors are the third leading cause of death in the United States. The use of AI technologies promises to reduce the cognitive workload for physicians, thus improving care, diagnostic accuracy, clinical and operational efficiency, and the overall patient experience. While there are understandable concerns and discussion about AI taking over human jobs, there is limited evidence to date that AI will replace humans in healthcare. The use of the artificial intelligence in the treatment process has been in a rise in the recent years. AI use is important and can be used in the process of trying to create the perfect recipe in the treatment process. Research shows that it is apparent that the use of the artificial intelligence provided something different from the typical known mechanism in the treatment process. In fact, the use of this system was found to have outdone the use of the current treatment system which is the treatment as usual TAU (Bennett, & Hauser, 2017). It is the case rate and the fee-for-service models which are used in the healthcare sector today. With this, the expected cost per unit of the outcome change was recorded to be at $189 vs. $497 for artificial intelligence and the older system. In this case, the use of the artificial intelligence in the treatment process was found to obtain an increase in the patient’s outcomes. And an increase in the overall outcome of 30 to 35 percent would be seen in the process (Bennett, & Hauser, 2017). Additionally, overall improvement was evident when the system was used as well as an approximate 50% improvement in outcome. These are just a few components which indicate that the system of the system is resulting in better and improved values (Bennett, & Hauser, 2017). AI is offering a better option in the treatment process which was not created before.
I believe that the introduction of the AI in the treatment process is just one of the steps that can be seen and be attributed to the better services which are occurring in the medical field. Starting from the ability to conduct a better diagnostic the use of AI provided a better mechanism that would be sued in the process of treating the human complications. It is providing something unique that would be sued in the process of making sure that people are benefiting from the technological areas. The use of the traditional model was complicated and time-consuming, but the current system will offer a certain new avenue which has not been in place. It is all because the use of technology in the most case provide a common ground. The common ground is the fact that better and quality services are offered on timely and selective manner. These are an element which can be used in the treatment process. Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. AI can be applied to various types of healthcare data. Major disease areas that use AI tools include cancer, neurology and cardiology (Darcy AM, Louie AK, & Roberts LW. , 2016). AI is a great tool, what may have sounded like science fiction years ago is a reality today. From pills that provide feedback about medication adherence or help doctors diagnose patient symptoms, to machine learning applications for data sharing, predictive modeling, and medical decision-making, advancements in healthcare AI deliver a constant source of innovation and excitement. The use of the system in the treatment process is valid and should be used in core developments in the healthcare sector.
A Bible verse that comes to mind is “Do not be conformed to this world, but be transformed by the renewal of your mind, that by testing you may discern what is the will of God, what is good and acceptable and perfect. ~ Romans 12:2 ESV
Bennett, C., & Hauser, K. (2017). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Retrieved 8 May 2017
Darcy AM, Louie AK, & Roberts LW. (2016). Machine Learning and the Profession of Medicine. JAMA 2016; 315:551–2.doi:10.1001/jama.2015.18421
Lamanna, C., & Byrne, L. (2018). Should Artificial Intelligence Augment Medical Decision Making? The Case for an Autonomy Algorithm. AMA J Ethics,20(9), E902-910. doi:10.1001/amajethics.2018.902.
Sigaud, O., & Buffet, O. (2010). Markov Decision Processes in Artificial Intelligence. Retrieved October 14, 2018, from https://zodml.org/sites/default/files/Markov_Decision_Processes_and_Artificial_Intelligence.pdf