Discussion Post Replies
Instructions: The response is a substantive interaction that builds on the ideas of others, delving deeper into the discussion question and course content in response to a colleague. The response includes one reference from a professional peer-reviewed scholarly journal.
Each reply should at least be a paragraph long.
Reply to AnaLuz
Artificial Intelligence in Healthcare
Big data and artificial intelligence (AI) are more and more used in medicine, either in prevention, diagnosis, or treatment (Brault & Saxena, 2021). Statistical algorithms can set a foundation of information for the use of AI in healthcare by allowing it to predict and prevent certain conditions. It can be of great benefit to have AI assist nurses in performing their non-nursing tasks. Some examples of nursing functions that AI can assist include ambulation support, vital signs measurement, and medication administration (Robert, 2019). These tasks usually require a lot of time, and with the assistance of AI, the nurse will be able to spend more time utilizing their expertise to care for the patient. Artificial intelligence encompasses the techniques used to teach computers to learn, reason, perceive, infer, communicate, and make decisions like or better than humans by learning a set of algorithms that are entered in its system (Robert, 2019). If the algorithms taught are biased in nature, then the activity performed by AI will consequently be biased. Although classic statistical methods are frequently used for interpretation, sometimes they are incapable of dealing with statistical inconsistencies, such as ethical dilemmas or contradicting evidence in practice (Chen, 2020). One specific example of implicit bias was observed in the Netflix show “Coded Bias” which aired on April 5, 2020 (Kantayya, 2020). This documentary found that the makers of facial recognition were mainly Caucasian males. Thus, they failed to incorporate the features of an African American female. In healthcare, such biases are insensitive to the size of the sample, even if the sample is the whole population, artificial intelligence and big data cannot avoid biases and even tend to increase them (Brault & Saxena, 2021).
Brault, N., & Saxena, M. (2021). For a critical appraisal of artificial intelligence in healthcare: The problem of bias in Health. Journal of Evaluation in Clinical Practice, 27(3), 513–519. https://doi.org/10.1111/jep.13528
Chen, L. (2020). Artificial intelligence for drug development, precision medicine, and healthcare. Biometrics, 76(4), 1392–1394. https://doi.org/10.1111/biom.13390
Kantayya, S. (2020, April 5). Coded Bias [Video]. Netflix. https://www.netflix.com/title/81328723
Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30–39. https://doi.org/10.1097/01. (Links to an external site.)NUMA. (Links to an external site.)0000578988.56622.21
Reply to Diana TV
Biasness in the use of AI is a major concern for most medical practitioners. When a computer algorithm is trained using biased data, it is expected that the results obtained by the algorithm would be biased. Biasness can be achieved through three areas; framing the task, using flawed data in training the system, and selecting data in a biased manner (Robert,2019). An example of a biased system is the health care system in the U.S (Obermeyer et al.,2019). This system uses commercial algorithms in guiding health decisions. However, in the algorithm, black patients were sicker than white ones while they were assigned the same risk level (Obermeyer et al.,2019). This system also reduces the number of black patients that are eligible for treatment by half. This bias may seem to be insignificant but has a lot of consequences for those affected.
AI is new dawn as they have been brought to deal with the different problems affecting people. The Rothman index has been beneficial to nurse practitioners. Even though the system got a lot of skepticism on the actionability of its results, over the years, it has become accepted to be more accurate as compared to nurses when identifying risks that affect patients (Robert,2019). The system has been effective in determining the level of complications that the patients have. The system can also be used to monitor changes (Robert,2019). This has proven effective in helping the nurses to manage their time too.
Ethics in the use of AI is a major concern among many people. The algorithms can be biased to give the desired result. An example is social marketing. It is alleged that the U.S election and the U.K Brexit referendum were manipulated using data collected from Facebook (Marr,2018). This is a case where AI has been used in social manipulation. This is done by collecting the data and sending the data that they find beneficial to the organizations (Marr,2018). This also shows an invasion of privacy.
Marr, B. (2018). Is Artificial Intelligence Dangerous? 6 AI risks everyone should know about.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.), 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30.https://doi.org/10.1097/01.NUMA.0000578988.56622.21
Reply to Pamela
Artificial Intelligence in Healthcare
Artificial intelligence can transform healthcare practice with increasing ability to translate the uncertainty and restraint in data extraction through imperfect clinical decisions or through its suggestions (Asan et al., 2020). The use of AI will require a certain amount of trust from participants including stakeholders to ensure the technology is in collaboration with the healthcare organizations policy and guidelines. Healthcare innovation driven by AI and is dependent upon the organization’s knowledge and capabilities of its provision to improve patient care and outcomes. AI being a major influence on today’s healthcare promotion should allow nurses and other healthcare professional to implement practice and standards that are directly related to nursing process. Having AI systems to improve healthcare is an inviting innovation however there are noted ethical issues that should be addressed. Ethical issues that could negatively impact healthcare include transparency, confidentiality, and accountability. For instance, a patient receiving a new diagnosis may want more information and need further understanding of its threat to their overall well-being may encounter mistrust with machine intelligence inability to possess a sense of transparency. Deep learning algorithm and even physicians who are generally familiar with this may be unable to provide information (Davenport & Kalakoda, 2019). To add, the use of AI may introduce biases regarding gender and race that negatively impacts the future of healthcare organizations. Although it is predicted that AI will transform nursing across all domains of nursing such as, administration, nursing practice, patient care, and research (Buchanan et al., 2021) nurses need to be prepared for its potential drawbacks to healthcare promotion.
Davenport T., Kalakota R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2). 94-98.
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted influences of artificial intelligence on nursing education: scoping review. JMIR NURSING, 4(1), e23933. https://doi.org/10.2196/23933
Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: focus on clinicians. Journal of Medical Internet Research, 22(6), e15154. https;//doi.org/10.2196/15154