This is the criteria for the paper: A. Final Draft Guidelines DIRECTIONS: Refer to the list below throughout the writing process. Do not submit your Touchstone until it meets these guidelines. Refer to the Sample Touchstone for additional guidance on structure, formatting, and citation.

1. Editing and Revising Have you significantly revised the essay by adjusting areas like organization, focus, and clarity? Have you made comprehensive edits to word choice, sentence variety, and style? Have your edits and revisions addressed all the feedback provided by your evaluator?

2. Cohesion and Source Integration Is the information presented in a logical order that is easy for the reader to follow? Have you included smooth transitions between sentences and paragraphs? Have you introduced your sources clearly and in a way that demonstrates their validity to the reader? Are your sources formatted correctly following APA style?

3. Conventions and Proofreading Have you double-checked for correct formatting, grammar, punctuation, spelling, and capitalization? Have you ensured that any cited material is represented accurately and with page/paragraph numbers?

4. Reflection Have you displayed a clear understanding of the revision process? Have you answered all reflection questions including specific and concrete examples that provide thoughtful insight in all responses? Are your answers included on a separate page below the composition? B. Reflection Questions DIRECTIONS: Below your assignment, include answers to all of the following reflection questions.

How much time did you spend revising your draft? What revision strategies did you use, and which worked best for you? (2-3 sentences) List three concrete revisions that you made and explain how you made them. What problem did you fix with each of these revisions? Issues may be unity, cohesion, rhetorical appeals, content, or any other areas on which you received constructive feedback. (4-5 sentences) What did you learn about your writing process or yourself as a writer? How has your understanding of the research process changed as a result of taking this course? (2-3 sentences) C. Rubric Advanced (100%) Proficient (85%) Acceptable (75%) Needs Improvement (50%) Non-Performance (0%) Revising Demonstrate comprehensive "re-visioning" of the composition. (27%) There is evidence of comprehensive re-visioning of the draft composition, including adjustments to organization, focus, clarity, and/or unity where needed or appropriate. There is evidence of significant re-visioning of the draft composition, including adjustments to organization, focus, clarity, and/or unity where needed or appropriate. There is evidence of some re-visioning of the draft composition, including adjustments to organization, focus, clarity, and/or unity where needed or appropriate; however, a few areas need some additional revision. There is little evidence of re-visioning of the draft composition, such that multiple areas in need of changes were unaltered. Revisions are absent or did not address the issues in the essay. Editing Demonstrate comprehensive sentence-level edits throughout the composition. (27%) There is evidence of comprehensive edits to the draft composition, including adjustments to word choice, sentence completeness, sentence variety, and/or style where needed or appropriate. There is evidence of substantial edits to the draft composition, including adjustments to word choice, sentence completeness, sentence variety, and/or style where needed or appropriate. There is evidence of some edits to the draft composition, including adjustments to word choice, sentence completeness, sentence variety, and/or style where needed/appropriate; however, some issues were overlooked. There is little evidence of edits made to the draft composition, such that many errors remain. Edits are absent or did not address the issues in the essay. Source Integration Integrate source material appropriately and effectively. (13%) Introduces sources smoothly and effectively through direct quotation, paraphrase, or summary. Primarily introduces sources effectively through direct quotation, paraphrase, or summary. Introduces some sources effectively through direct quotation, paraphrase, or summary, but more variety could be used. Relies too heavily on one method of source integration (direct quotation, paraphrase, or summary); does not thoughtfully apply source integration techniques. Shows no attempt to integrate source material into the composition or relies on quoted source material for over half of the composition. Cohesion Establish and maintain a logical flow. (13%) Sequences ideas and paragraphs logically and uses smooth transitions (within and between paragraphs) such that the reader can easily follow the progression of ideas. Sequences ideas and paragraphs logically and uses transitions (within and between paragraphs) such that the reader can easily follow the progression of ideas. Primarily sequences ideas and paragraphs logically and uses sufficient transitions (within and between paragraphs) such that the reader can generally follow the progression of ideas. The progression of ideas is often difficult to follow, due to poor sequencing, ineffective transitions, and/or insufficient transitions. The progression of ideas is consistently difficult to follow, due to poor sequencing and lack of transitions. Conventions and Proofreading Demonstrate command of standard English grammar, punctuation, spelling, capitalization, and usage. (13%) There are few, if any, negligible errors in grammar, punctuation, spelling, capitalization, formatting, and usage. There are occasional minor errors in grammar, punctuation, spelling, capitalization, formatting, and usage. There are some significant errors in grammar, punctuation, spelling, capitalization, formatting, and usage. There are frequent significant errors in grammar, punctuation, spelling, capitalization, formatting, and usage. There are consistent significant errors in grammar, punctuation, spelling, capitalization, formatting, and usage. Reflection Answer reflection questions thoroughly and thoughtfully. (7%) Demonstrates thoughtful reflection; consistently specific and concrete examples that provide thoughtful insight, following or exceeding response length guidelines. Demonstrates thoughtful reflection; includes multiple specific and concrete examples that provide thoughtful insight, following response length guidelines. Primarily demonstrates thoughtful reflection, but some responses are lacking in detail or insight; primarily follows response length guidelines. Shows limited reflection; the majority of responses are lacking in detail or insight, with some questions left unanswered or falling short of response length guidelines. No reflection responses are present. D. Requirements The following requirements must be met for your submission to be graded:

Composition must be 6-8 pages (approximately 1500-2000 words, not including your references or reflection question responses). Double-space the composition and use one-inch margins. Use a readable 12-point font. All writing must be appropriate for an academic context. Composition must be original and written for this assignment. Use of generative chatbot artificial intelligence tools (ChatGPT, Bing Chat, Bard) in place of original writing is strictly prohibited for this assignment. Plagiarism of any kind is strictly prohibited. Submission must include your name, the name of the course, the date, and the title of your composition. Include all of the assignment components in a single file. Acceptable file formats include .doc and .docx. This is my paper: The Transformative Potential of Artificial Intelligence in Healthcare

Artificial Intelligence (AI) has emerged as a groundbreaking force in the healthcare industry, promising to revolutionize diagnosis, treatment, and management of medical conditions. "Artificial Intelligence and Machine Learning have emerged as critical technologies in 2023, helping to transform diagnosis, treatment, patient outcomes and operational efficiency," reports ModuleMD (Rajabasha, 2023). This transformation is driven by AI's enhancement of diagnostic accuracy, its significant role in patient care improvement, and the heightened efficiency it brings to healthcare systems. AI is revolutionizing the healthcare industry by significantly enhancing diagnostic accuracy, personalizing patient care, and streamlining operational efficiency. Despite facing challenges in integration and ethical considerations, AI's potential to reshape healthcare delivery is significant, as it promises more accurate diagnoses, individualized treatments, and improved healthcare operations. However, it is crucial to address the ethical and practical concerns to fully harness these benefits.

One of the most compelling advantages of AI in healthcare is its ability to rapidly and accurately diagnose diseases. For instance, AI algorithms have demonstrated remarkable accuracy in interpreting medical images, sometimes surpassing human experts. A study published in Nature Medicine found that an AI system developed by Google Health was able to identify breast cancer in mammograms with greater accuracy than radiologists, reducing false positives by 5.7% and false negatives by 9.4% (McKinney et al., 2020). Langlotz (2023) insightfully points out, "AI can be, in some ways, superhuman because of its ability to link disparate data sources. It can take genomic information and imaging information and potentially find linkages that humans are not able to make" (Stanford Medicine, 2023). This technological advancement reduces the margin of error in medical assessments, which significantly improves patient outcomes. For example, AI's implementation in the analysis of lung CT scans has shown to reduce diagnostic errors by 50% (Lakhani, 2021). In medical imaging, for instance, AI's impact is profound. Deep learning algorithms excel in image recognition, significantly enhancing the detection of conditions such as cancer and fractures, which underscores their vital role in early disease intervention. For example, deep learning models have improved the accuracy of radiology images, reduced false negatives, and aided in the timely detection of medical issues. "AI-driven tools are becoming indispensable in the field of radiology, enhancing the accuracy and efficiency of diagnoses," highlights a study in the American Journal of Kidney Diseases (Wilson, 2020). The formation of the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) has spurred significant advancements in the field, with 85% of the over 500-plus FDA-approved AI algorithms focused on imaging, underscoring AI's diagnostic superiority. In specialized healthcare sectors like nephrology, the impact of AI is equally transformative. Dr. Peter Kotanko's work at the Renal Research Institute and Icahn School of Medicine at Mount Sinai exemplifies this. Kotanko (2023) delves into the utilization of AI and machine learning in interpreting radiology and histopathology images, along with smartphone images, for diagnosing various conditions. He states, "AI/ML-supported image analysis is used in the radiological assessment of renal abnormalities, histological analysis of renal biopsies, classification of arteriovenous fistula aneurysms, and kidney stones" (Kotanko, 2023, p. 2, para. 7). The early detection capability, facilitated by AI, opens pathways to more effective treatments and notably enhances patient survival rates. Reflecting similar advancements, Stanford Medicine (2023) highlights how AI innovations, particularly in imaging technologies used in cardiology and oncology, are revolutionizing these fields, stating, "These technological advances in imaging are not only improving clinical outcomes but are also significantly reducing the time needed for diagnosis" (Stanford Medicine, 2023, p. 2). Together, these advancements demonstrate the profound impact of AI in enhancing diagnostic accuracy and improving patient outcomes across various medical disciplines.

Moreover, AI-driven technologies substantially elevate the patient's experience by ensuring personalized treatment plans and fostering more effective communication with healthcare providers. This broad application across multiple medical disciplines underscores AI's far-reaching and transformative potential beyond standard healthcare practices. "The application of technology and artificial intelligence (AI) in healthcare has the potential to address some of these supply-and-demand challenges," highlights the Future Healthcare Journal (Bajwa, Munir, Nori, & Williams, 2021), demonstrating AI's role in improving the overall patient experience. The deployment of AI in healthcare brings forth complex ethical issues, including concerns about patient privacy, data misuse, and algorithmic bias. Ensuring that AI systems are developed and used responsibly is crucial. "Ethical AI usage in healthcare must prioritize patient confidentiality, informed consent, and transparency" (npj Digital Medicine, 2020), notes Mesk and Grg in their exploration of medical ethics in the AI era. It is imperative that healthcare providers and AI developers work together to create guidelines that safeguard patient interests and promote fairness.

Yet, integrating AI into healthcare is not without its challenges. A notable concern is the potential skill degradation among healthcare professionals. Critics argue that an over-reliance on AI for diagnoses and treatment planning might erode the diagnostic skills and clinical judgment of medical practitioners. This concern is particularly relevant as AI tools become increasingly integrated into daily medical practice. As stated in the Future Healthcare Journal (2021), "We hold the view that AI amplifies and augments, rather than replaces, human intelligence. Hence, when building AI systems in healthcare, it is key to not replace the important elements of the human interaction in medicine but to focus it and improve the efficiency and effectiveness of that interaction" (Bajwa et al., 2021). This puts the focus on preserving and enhancing professional medical expertise indicating that a balanced approach is crucial for the adoption of AI technologies. AI's role in enhancing patient engagement through personalized apps and tools is transforming the patient-provider relationship. These tools help in managing chronic conditions, medication adherence, and lifestyle changes by providing personalized feedback and support. "AI applications are crucial in fostering an engaged and informed patient population, which is fundamental to the success of modern healthcare" (ModuleMD, 2023), elaborates Rajabasha. In response to this, it is critical to view AI as an augmentative tool rather than a replacement for medical expertise. Implementing continuous training and education programs ensures that healthcare professionals maintain their diagnostic skills and clinical judgment while effectively utilizing AI tools. This synergistic approach positions AI as a collaborative partner in healthcare delivery, enhancing rather than undermining professional competence.

The economic implications of AI in healthcare are profound. AI technologies can streamline operations, reduce costs, and improve service delivery, leading to better patient outcomes and lower healthcare costs overall. "The introduction of AI in healthcare settings has shown a promising decrease in operational costs and improved treatment outcomes," states Davenport and Kalakota (Future Healthcare Journal, 2019). Additionally, investment in AI healthcare startups has surged, indicating strong market confidence in the technology's potential to revolutionize the industry. Additionally, AI plays a pivotal role in facilitating remote monitoring and telemedicine, especially beneficial for individuals with chronic conditions who require ongoing monitoring. This not only enhances patient convenience and accessibility to care but also alleviates the strain on healthcare facilities. Geller (2020, para. 1) emphasizes this, noting that AI "has equipped healthcare institutions and practitioners with tools to alleviate workloads and redefine workflows" (Geller, M. 2020, December 7), thus playing a crucial role in the transformation of healthcare delivery. Supporting this, Dr. Peter Kotanko highlights the effectiveness of AI in remote patient monitoring systems, especially in managing chronic diseases like kidney disease. He points out that "AI is helping doctors diagnose and manage kidney disease and predict trajectories for kidney patients," and importantly, "AI not only relies on structured lab data or data stored in electronic health records, but also, of course, uses tools like natural language processing to extract insights from the unstructured texts" (Kotanko, 2021). This underscores the importance of AI in providing real-time data analysis for better patient care. Despite its benefits, the integration of AI into healthcare systems faces significant challenges, such as data interoperability and the need for extensive staff training. Developing standard protocols and continuous professional education are essential steps towards overcoming these barriers. "Standardization of data formats and intensive training programs for healthcare professionals are essential for the successful integration of AI technologies," notes Geller (Geller, M. (2020, December 7).

However, AI's application in healthcare raises critical concerns regarding data security and patient privacy. The reliance on large datasets of sensitive information necessitates robust cybersecurity measures and strict adherence to data protection regulations. This is crucial in safeguarding patient confidentiality against potential data breaches. This proves that AI systems must be designed with advanced security protocols to ensure data safety and confidentiality. Additionally, the healthcare sector's vulnerability to cyberattacks, including ransomware, heightens the importance of AI in preemptively identifying and mitigating cybersecurity threats. Geller (2020, para. 1) notes, "AI has provided healthcare institutions and practitioners with tools that they can use to reduce pressure on their workloads and redefine their workflows" Geller, M. (2020, December 7), thus ensuring the security and resilience of healthcare organizations. Supporting the need for robust data protection, Dr. Peter Kotanko emphasizes the utilization of AI in handling sensitive data, pointing out that "AI not only relies on structured lab data or data stored in electronic health records but also uses tools like natural language processing to extract insights from the unstructured texts" (Kotanko, P., & Nadkarni, G. N. 2023), thereby underlining the critical role of maintaining stringent security measures in AI applications to safeguard patient information. Kass-Hout (2023) emphasizes, "AI is playing a pivotal role in transforming healthcare delivery through innovations in machine learning and natural language processing" (HealthTech Magazine).

Furthermore, AI's predictive modeling capabilities extend to various medical conditions, including heart disease, sepsis, and diabetes. Particularly in managing chronic diseases like kidney disease, AI's role is increasingly pivotal. Jiang et al. (2017) highlight AI's utility in healthcare, stating, "AI can use sophisticated algorithms to 'learn' features from a large volume of healthcare data, and then use the obtained insights to assist clinical practice" (Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. 2017), This statement aligns with the focus on AI's role in enhancing healthcare through predictive modeling and data analysis. Adding to this, Wilson (2020) notes, "In terms of the sheer number of data points analyzed, this is one of the largest studies of machine learning in medicine to date" (Wilson, F. P. 2020), highlighting AI's significant role in enhancing healthcare diagnostics and management, especially in kidney disease and similar medical conditions. The integration of AI into various medical fields marks it as a transformative technology in modern healthcare. Its influence extends beyond diagnostics and treatment; AI reshapes administrative tasks, predictive analytics, and patient engagement, offering a comprehensive transformation of the healthcare landscape. Ethical considerations remain paramount in this evolution, with data privacy, informed consent, and the elimination of bias in AI algorithms taking center stage. Patients' medical information requires rigorous protection, and compliance with regulations like HIPAA is essential to maintain trust. Moreover, transparency in AI algorithms and decision-making processes is crucial to avoid healthcare disparities and ensure equitable healthcare access. Beyond the technological prowess of artificial intelligence, its capacity to humanize healthcare delivery is one of its most celebrated attributes. As Dr. Eric J. Topol argues in "Deep Medicine", reviewed by Hagen J.B., AI is redefining the patient-care provider relationship by automating routine tasks, thus freeing up medical professionals to focus more on direct patient care. This shift enhances the quality of care, increases patient satisfaction, and restores the emphasis on compassion and empathy in healthcare interactions. Topol's perspective, as elucidated by Hagen, underscores the transformative potential of AI to make healthcare more efficient and human-centered, aligning technology with the core values of medical practice (Hagen, 2019). As we navigate these transformations, it is essential to recognize that the integration of AI in healthcare impacts more than just medical procedures and it significantly influences health policy, patient education, workforce training, and ethical considerations, promising a future where high-performance, personalized medicine becomes the norm.

The integration of AI in healthcare also involves navigating initial costs, infrastructure upgrades, staff training, and interoperability issues with existing electronic health records (EHRs). As Geller (2020) asserts, "The technology isn't completely in its infancy right now, but it does need to be refined and adapted consistently as lessons are learned and algorithms become more adept" (Geller, M. 2020, December 7). This evolution is critical in addressing ethical considerations and ensuring AI is used equitably and effectively in healthcare.

Looking to the future, AI in healthcare promises further revolution. "AI can be, in some ways, superhuman because of its ability to link disparate data sources," said Curtis Langlotz, MD, PhD, professor of radiology, of medicine and of biomedical data science as well as the director of the Center for Artificial Intelligence in Medicine and Imaging. "It can take genomic information and imaging information and potentially find linkages that humans aren't able to make" (Langlotz, 2023). AI's role in interpreting genomic data is particularly promising, leading to more personalized treatment options. Moreover, AI can assist healthcare organizations in optimizing resource allocation, reducing healthcare disparities, and improving population health management, highlighting its vast potential in transforming and advancing healthcare.

One of the most exciting developments in AI in healthcare is its expansion beyond traditional medical boundaries. AI is not just transforming clinical practices but is also revolutionizing areas like health policy making, patient education, and preventive medicine. For instance, AI algorithms are being used to analyze large-scale health data to identify trends and patterns that can inform public health strategies and policies. As Kass-Hout (2023) from HealthTech Magazine notes, AI is "at the forefront of major innovations in data processing and analysis, which in turn supports significant public health decisions" (Kass-Hout, 2023). These insights are invaluable in addressing widespread health issues, such as the management of pandemics or the allocation of healthcare resources.

Moreover, AI is playing a significant role in patient education and engagement. According to Rajabasha (2023), "Personalized AI-driven apps and tools are making it easier for patients to understand their health conditions and treatment options, effectively democratizing medical knowledge" (Rajabasha, 2023). By empowering patients with information and support, AI is helping to promote more proactive and informed healthcare decisions. Another crucial area where AI is making an impact is in the healthcare workforce and training. AI is not only changing the roles of existing healthcare professionals but also creating new roles and opportunities. Bajwa et al. (2021) explain that "AI requires a new cadre of data scientists and AI specialists to innovate and manage these complex systems within healthcare settings" (Bajwa, Munir, Nori, & Williams, 2021). Additionally, as AI tools become more prevalent, there is a growing demand for healthcare professionals who are skilled in using these technologies. This shift is leading to changes in medical education and training programs, which are now beginning to incorporate AI and data science into their curricula. Furthermore, AI is also being used as a training tool for healthcare professionals. Jiang et al. (2017) describe how "AI-driven simulations and virtual reality (VR) environments are providing medical students and professionals with realistic, hands-on experience without the risks associated with real-life procedures" (Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. 2017). These advanced training tools are enriching the learning experience and boosting the skills of future healthcare providers.

Despite the potential of AI in healthcare, scaling these innovations across different healthcare systems poses significant challenges. One major challenge is the variability in healthcare infrastructure and technology adoption among different countries and regions. While some countries have advanced healthcare systems that AI can be seamlessly intergrated, others may lack the necessary infrastructure, making it challenging to implement AI solutions effectively. Another challenge is the issue of interoperability, which refers to the ability of different AI systems and healthcare technologies to work together seamlessly. The lack of standardization in healthcare data formats and AI systems can hinder the integration of AI into existing healthcare infrastructures. Addressing these interoperability challenges is crucial for the widespread adoption of AI in healthcare.

As AI continues to advance, it brings with it a host of ethical implications that must be carefully considered. Issues such as algorithmic transparency, accountability, and the potential for AI to reinforce existing healthcare disparities are of paramount concern. Ensuring that AI systems are fair, unbiased, and transparent is essential for maintaining public trust and ethical integrity in healthcare. Looking towards the future, continuous advancements in AI technology, coupled with an increasing emphasis on personalized medicine, suggest that AI will play an even more significant role in healthcare. Innovations in areas such as genomics, personalized drug development, and precision medicine are likely to be accelerated by AI, leading to more effective and tailored healthcare solutions. The future integration of AI into healthcare looks promising with continuous technological advancements. AI's potential to advance precision medicine is especially significant, as it could enable highly personalized treatment plans tailored to individual profiles. "Advancements in AI technology are expected to drive the growth of personalized medicine, offering treatments tailored to individual genetic makeup," suggests Topol (Deep Medicine, 2019). This could drastically improve treatment efficacy and patient satisfaction.

In conclusion, the integration of Artificial Intelligence in healthcare is reshaping more than just medical diagnostics and treatment. It extends to influencing health policy, enhancing patient education, revolutionizing workforce training, and navigating complex ethical landscapes. By embracing these technological advancements and addressing the associated challenges, the healthcare sector can fully exploit the potential of AI. This promises a future where high-performance, personalized medicine becomes the norm, significantly improving patient outcomes. As we move forward, it is crucial to ensure that AI and human expertise work synergistically, not only to advance medical technology but also to uphold and enhance the quality of care delivered to every patient. Dr. Topol's insights remind us of the delicate balance required to harness AI's full potential responsibly, paving the way for a healthcare system that is both innovative and empathetic (Topol, 2019).

References

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Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94

Geller, M. (2020, December 7). The benefits of AI in Healthcare - pros and cons. Aidoc.

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