352014 ai report

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The University of Dodoma Quick Submit Quick Submit oo Oo Oo Document Details Submission ID trn:oid:::1:2746791150 Submission Date Nov 10, 2023, 4:46 PM GMT+2 Download Date Nov 10, 2023, 4:48 PM GMT+2 File Name 352014_Artificial_Intelligence_AI_in_Health.docx File Size 20.2 KB 6 Pages 1,332 Words 8,062 Characters Page 1 of 8 - Cover Page Submission ID trn:oid:::1:2746791150 Page 1 of 8 - Cover Page Submission ID trn:oid:::1:2746791150

How much of this submission has been generated by AI? 0% of qualifying text in this submission has been determined to be generated by AI. Caution: Percentage may not indicate academic misconduct. Review required. It is essential to understand the limitations of AI detection before making decisions about a student's work. We encourage you to learn more about Turnitin's AI detection capabilities before using the tool. Frequently Asked Questions What does the percentage mean? The percentage shown in the AI writing detection indicator and in the AI writing report is the amount of qualifying text within the submission that Turnitin's AI writing detection model determines was generated by AI. Our testing has found that there is a higher incidence of false positives when the percentage is less than 20. In order to reduce the likelihood of misinterpretation, the AI indicator will display an asterisk for percentages less than 20 to call attention to the fact that the score is less reliable. However, the final decision on whether any misconduct has occurred rests with the reviewer/instructor. They should use the percentage as a means to start a formative conversation with their student and/or use it to examine the submitted assignment in greater detail according to their school's policies. How does Turnitin's indicator address false positives? Our model only processes qualifying text in the form of long-form writing. Long-form writing means individual sentences contained in paragraphs that make up a longer piece of written work, such as an essay, a dissertation, or an article, etc. Qualifying text that has been determined to be AI-generated will be highlighted blue on the submission text. Non-qualifying text, such as bullet points, annotated bibliographies, etc., will not be processed and can create disparity between the submission highlights and the percentage shown. What does 'qualifying text' mean? Sometimes false positives (incorrectly flagging human-written text as AI-generated), can include lists without a lot of structural variation, text that literally repeats itself, or text that has been paraphrased without developing new ideas. If our indicator shows a higher amount of AI writing in such text, we advise you to take that into consideration when looking at the percentage indicated. In a longer document with a mix of authentic writing and AI generated text, it can be difficult to exactly determine where the AI writing begins and original writing ends, but our model should give you a reliable guide to start conversations with the submitting student. Disclaimer Our AI writing assessment is designed to help educators identify text that might be prepared by a generative AI tool. Our AI writing assessment may not always be accurate (it may misidentify both human and AI-generated text) so it should not be used as the sole basis for adverse actions against a student. It takes further scrutiny and human judgment in conjunction with an organization's application of its specific academic policies to determine whether any academic misconduct has occurred. Page 2 of 8 - AI Writing Overview Submission ID trn:oid:::1:2746791150 Page 2 of 8 - AI Writing Overview Submission ID trn:oid:::1:2746791150

Surname 1 Student’s Name Instructor’s Name Course Institution Date Artificial Intelligence (AI) in Health Artificial Intelligence (AI) is now a changing factor in several industries, and healthcare is one of the industries. There are many applications of AI in medical settings, for instance, machine learning (ML) algorithms. Technology is now being incorporated into healthcare because it is a factor that ensures accuracy, efficiency, competency, and good record-keeping within medical settings. AI comprises machines and computers imitating human cognition, decision making, thinking, and the capability of learning based on medical data analysis. Therefore, this is an informative essay exploiting the importance of Artificial Intelligence in healthcare, its benefits, applications, ethical implications, challenges, and the future of AI in healthcare. AI's usage in medical diagnosis is one of the field's primary use cases. Artificial intelligence (AI) assists healthcare providers in making more precise diagnoses and creating individualized treatment plans by utilizing patient data and other relevant information. AI's capacity to sort through large volumes of data makes predictive and proactive healthcare possible by enhancing recommendations for preventive care through big data analysis. Using AI into medical operations is a crucial first step in improving overall patient outcomes (Manickam et al., p. 562). The ability of AI to predict and track the emergence of infectious diseases is just another indication of its potential in the healthcare industry. By employing data analysis from a range of sources, including government and healthcare databases, artificial Page 3 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150 Page 3 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150

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Surname 2 intelligence plays a critical role in maintaining global public health by averting pandemics and epidemics. Here are some of the examples and types of AI applied in healthcare. Algorithms are trained on datasets such as medical records in machine learning (ML) to construct models that can classify data and predict outcomes. A subfield of machine learning called "deep learning" leverages bigger datasets and more complex algorithms to tackle challenging problems (Schwalbe et al., p. 1579). On the other hand, natural language processing, or NLP, focuses on understanding both written and spoken human language. Robotic process automation (RPA) is also used to automate clinical and administrative operations, which improves patient outcomes overall. Since there is the continuous advancement of technology, the application of AI in healthcare is also expanding. ML algorithms-driven health care analytics provide insights from past data to enhance decision-making and maximize health outcomes. Another noteworthy use of AI is precision medicine, which creates individualized treatment regimens based on a patient's genetic composition, lifestyle, environmental variables, and medical history. AI- enabled predictive models give medical personnel the ability to estimate a patient's risk of developing a particular condition. Furthermore, AI helps diagnose diseases like malignant tumors by interpreting medical tests like MRIs and X-rays. All of these applications work together to improve the effectiveness and efficiency of healthcare delivery. The incorporation of Artificial Intelligence in the industry of healthcare has bear fruit and is now making the healthcare industry more effective. There are numerous advantages of using AI in healthcare for healthcare providers, patients, and the system as a whole. For healthcare professionals, AI-driven improved decision-making and streamlined automated services translate into cheaper operating expenses (Guo, p. e18228). AI can be used by providers to create personalized treatment plans and detect illnesses more quickly and precisely Page 4 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150 Page 4 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150

Surname 3 than they could with just conventional techniques. The effectiveness of AI-driven health services may lead to improved health outcomes and maybe lower costs for patients. There are now more employment options as a result of the expanding benefits of AI and healthcare. Jobs like health informatics specialist, machine learning engineer, data scientist and AI engineer are becoming more and more frequent. People who are interested in the nexus between AI and healthcare might investigate these positions as the field grows and contribute to the continuous change in the sector. Ethical considerations are considered in these settings because AI’s role in healthcare involves decision making. Since humans have traditionally made the majority of healthcare choices, the use of smart technologies in this process presents questions about privacy, permission, accountability, and openness. Among the ethical issues that arise, transparency stands out as particularly difficult, particularly when deep learning algorithms are applied to picture analysis. These algorithms can be difficult to interpret, which makes it challenging to explain to patients why a specific diagnosis or choice was made (Davenport et a., p.94). It is difficult to ensure informed consent and patient understanding when there is a lack of transparency. There are some common mistakes made by AI systems, which are inevitability, especially in patient treatments and diagnosis, which also calls for ethical considerations. Taking responsibility for these mistakes becomes essential when you take into account the potentially catastrophic outcomes. Furthermore, there's the worry that patients might be given private medical information by AI systems, information they might rather hear from a human doctor who can understand them and provide context (Haleem et al., 235). The ethical environment is further complicated by the potential for algorithmic bias, whereby AI systems may inadvertently reinforce prejudices based on racial or gender stereotypes. Page 5 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150 Page 5 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150

Surname 4 In recent years, the healthcare industry has increasingly integrated AI, and they have experienced challenges that need to be addressed. Regulatory authorities, governmental bodies, and healthcare institutions have to come up with strong structures for monitoring essential ethical issues. A cautious and thorough approach to governance is necessary to minimize unfavorable effects and guarantee that the application of AI in healthcare is consistent with cultural norms (Davenport et al., 94). The development of interpretable and explicable AI algorithms should be the focus of efforts to address the transparency challenge. Encouraging research and development projects that concentrate on developing algorithms that offer transparent insights into their decision-making procedures would boost patient confidence and promote well-informed decision-making. Furthermore, in order to successfully convey AI- driven diagnosis and treatment plans to patients and promote openness and understanding, healthcare personnel who use AI should be properly trained. Errors committed by AI systems highlight how crucial it is to set up accountability measures. It is essential to have a framework for admitting mistakes, growing from them, and constantly enhancing AI systems. In addition to protecting patients, this proactive strategy advances the continuous improvement and dependability of AI applications in healthcare. A unified and moral approach to the application of AI in healthcare settings can be ensured by developing and enforcing ethical norms and guidelines throughout the sector. To sum up, artificial intelligence (AI) in healthcare has enormous promise, but there are real-world obstacles and ethical issues to be resolved. Clinical professionals fear that the incorporation of AI in healthcare will in future because they will lose their job since technology is taking over the health industry. That will not be the future incident since the aim of introducing AI in healthcare is to collaborate with healthcare professionals to increase efficiency in the delivery of healthcare. Human clinicians can focus their attention on tasks that require human talents alone, such as holistic, persuasive, and empathetic information Page 6 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150 Page 6 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150

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Surname 5 integration, as AI takes on regular and data-intensive duties. Overcoming a number of obstacles is essential for the effective integration of AI in clinical practice. Important factors that require attention are regulatory approval, smooth integration with Electronic Health Record (EHR) systems, standardization, healthcare professional training, and financial support. The institutional and systemic adoption of AI in healthcare is a complicated process that takes time and coordinated efforts, even while the technology themselves are developing quickly. Page 7 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150 Page 7 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150

Surname 6 Works Cited Davenport, Thomas, and Ravi Kalakota. "The potential for artificial intelligence in healthcare." Future healthcare journal 6.2 (2019): 94. Guo, Yuqi, et al. "Artificial intelligence in health care: bibliometric analysis." Journal of Medical Internet Research 22.7 (2020): e18228. Haleem, Abid, Mohd Javaid, and Ibrahim Haleem Khan. "Current status and applications of Artificial Intelligence (AI) in medical field: An overview." Current Medicine Research and Practice 9.6 (2019): 231-237. Manickam, Pandiaraj, et al. "Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare." Biosensors 12.8 (2022): 562. Schwalbe, Nina, and Brian Wahl. "Artificial intelligence and the future of global health." The Lancet 395.10236 (2020): 1579-1586. Page 8 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150 Page 8 of 8 - AI Writing Submission Submission ID trn:oid:::1:2746791150