PSA : DO NOT USE ai it will be checked Discussion Board Post (300 words minimum) Please complete i ...
PSA : DO NOT USE ai it will be checked Discussion Board Post (300 words minimum) Please complete in class: 1. Write a discussion board post outlining how AI can transform healthcare. 2. Identify a specific issue or challenge within the healthcare industry that AI can help address. 3. Provide an example of an AI system or technology that can be used to assist in solving the identified problem. 4. Support your arguments with references to at least three scholarly articles on AI in healthcare. Make sure to cite them properly. Commenting on Peers' Posts (200 words each, total of 3 comments for full credit) Please complete before the due date listed for this assignment: 1. Respond directly underneath the posts of three other students. 2. Provide constructive feedback or additional insights related to their discussion. 3. Support your comments with references to at least one scholarly article that supplements or reinforces the points made by your peers. Grading Rubric: Original Post (300 words): Clear articulation of how AI can impact healthcare (25 points) Identification and explanation of a specific healthcare problem that AI can address (15 points) Example of an AI system or technology relevant to the identified problem (15 points) Proper citation and integration of three scholarly articles (15 points) Comments on Peers' Posts (200 words each, total of 3 comments): (30 total points) Relevance and insightfulness of comments (20 points each) Appropriateness of scholarly references (10 points each) PEER POST TO COMMENT ON 1. BY :Marykate Pierre Ai in my opinion can transform healthcare exponentially. Ai can improve accuracy with diagnosing and improve efficiency within patients and improve accessibility around the world. There have been many instances where patients have been mis diagnosed that led to treatment that didn't improve the real condition they were in. Instead of this happening ai can analyze like genetics, lifestyle, and medical history within minutes. This allows for a much earlier intervention and diagnosis. A specific healthcare issue AI can address is cancer detection. There have been multiple cases of people who come in not knowing they have cancer. They often think problems that arise in their life that are cancer related. With ai it can early detect it. According to DR Kachaamy "Dr. Kachaamy has embraced AI, including a tool called GI Genius®, a companion program he runs during a colonoscopy. GI Genius uses information inputted from thousands of previous procedures to point him to potential areas of concern in the colon. From there, Dr. Kachaamy can determine if that are requires further investigation. This has been an excellent addition to our cancer-fighting arsenal,”(city of hope, 2024) A doctor that diagnoses stages of colon cancer uses AI to predict better treatment outcomes. It has this amazing ability to analyze large data sets , find correlations difficult to identify by current methods and analyze genetic mutations This allows AI to help doctors make sure treatment protocols are tailored to specific patients while minimizing risk of side effects and avoiding treatments that may be ineffective. Another way ai is improving cancer is its treatment. There have been multiple studies that show ai is being used to develop new treatments for cancer in a more efficient and faster way. With these new ways researchers can come up with many more plans to treat patients in new ways. "AI is being used in many ways to develop new treatments for cancer through novel approaches to drug discovery and design, drug repurposing, and predicting patient responses to treatment. NCI researchers and their colleagues have employed AI to help predict how immune cells called T cells will respond to tumors. They used machine learning to find patterns within a large volume of human and mouse T-cell activation data and predict T-cell behavior with the aim of improving immunotherapies." This can cut time in half for researchers so that they can figure more solutions for other diseases cutting time in half for them. Another tool ai has is robotic surgery.a robotic surgery system that offers a minimally invasive alternative to both open surgery and laparoscopy. Because robotic surgery, or robot-assisted surgery, requires only a few tiny incisions and offers greater vision, precision and control for the surgeon, patients often recover sooner, move on to additional treatments if needed, and get back to daily life quicker. There have been many proven benefits to robotic surgery some examples are "Reduced pain, Lower risk of infection or complications, Less blood loss (fewer transfusions), Shorter hospital stays Less scarring due to smaller incisions" (Marie marksman, md 2022) 2. BY : Mardoche Myotte The Transformative Potential of AI in Healthcare Artificial Intelligence (AI) is rapidly emerging as a transformative force in healthcare offering significant opportunities to enhance the quality, accessibility, and efficiency of services. By leveraging machine learning, natural language processing, and data analytics, AI has the potential to solve several long-standing challenges within the healthcare system, ranging from improving diagnostic accuracy to enhancing operational efficiency. One critical area where AI is already making substantial progress is in addressing the challenge of diagnostic errors, particularly in radiology. AI and Diagnostic Errors in Healthcare Diagnostic errors represent a major challenge in healthcare, contributing to significant morbidity, mortality, and unnecessary treatments. Studies suggest that diagnostic errors account for a substantial proportion of medical errors, leading to delayed or incorrect treatment (Singh et al., 2019). This issue is particularly pronounced in fields such as radiology, where human error can result in overlooked conditions such as cancers or fractures. AI technologies have the potential to alleviate this problem by improving the speed and accuracy of diagnostic processes. AI Technology: Computer-Aided Diagnosis (CAD) Systems one notable AI system that can help address diagnostic errors in radiology is the computer-Aided Diagnosis (CAD) system. CAD uses machine learning algorithms trained on vast datasets of medical images to assist radiologists in detecting anomalies such as tumors, lesions, or fractures. These systems analyze medical images like X-rays, CT scans, and MRIs and highlight potential areas of concern for further examination by a healthcare professional. For example, a deep learning-based AI model called “DeepRadiology” has shown promise in detecting various types of cancers, including breast cancer, by analyzing mammogram images with remarkable accuracy. Research by Estela et al. (2019) demonstrated that AI systems can match or even exceed the diagnostic performance of expert radiologists, suggesting that AI may reduce human error in diagnostics, leading to better patient outcomes. Supporting Arguments from Scholarly Articles 1. Improving Diagnostic Accuracy: A study by Choi et al. (2020) discusses how AI-based systems, particularly deep learning models, can improve the accuracy of medical diagnoses, especially in radiology. Their research shows that AI can analyze medical imaging with a high degree of precision, enabling more accurate and timely diagnoses, which could significantly reduce diagnostic errors. In Conclusion AI technologies are poised to revolutionize healthcare by addressing critical issues such as diagnostic errors, particularly in radiology. AI systems like CAD can enhance diagnostic accuracy, improve operational efficiency, and reduce human error, ultimately contributing to better patient care. As these technologies continue to advance, they will likely play an increasingly central role in transforming the healthcare landscape. 3. BY :Jada Walker Electronic and digital healthcare is on an incline and aims to transform healthcare in many ways. Artificial intelligence simplified is computer technology that imitates human behaviors like communication, reasoning, decision making, and learning. Implementation of these technological advancements in healthcare reduce time consuming tasks, and solve some challenges health professionals face within the industry. Artificial intelligence or machine and deep learning is used to examine and analyze large amounts of data. These include the patient's results and symptoms, identifying patterns and making predictions on nursing diagnoses. With these predictions the decision making process is reduced making more accurate and efficient healthcare outcomes. “AI algorithms reading and analyzing medical images like X-rays and MRI to quickly and more accurately identify early stages of diseases the human eye could miss” (Bulck, Couturier & Moons, 2023). Artificial intelligence is used in nursing to improve work load, enhance patient care, and efficient healthcare outcomes all while lowering costs. The implementation of AI in nursing has so far shown the benefits of information technology such as accurate diagnosis, identification of change in patient conditions, easier care coordination and communication. Some issues that health professionals come across are dealing with time consuming tasks that take away from hands-on patient care, and medication errors. These problems can be alleviated using AI technology such as Nuance and Curate.AI. The Nuance Dragon Ambient eXperience(DAX) saves time that would usually be spent on manual documentation of patient visits, while also improving workflow by recording and translating clinical notes directly from health professionals. “Natural language processing technology can automate administrative tasks such as documenting in EHR and optimizing clinical workflow, enabling clinicians to focus more time on caring for patients”(Bajwa, Munir, Nori & Williams, 2021). Therapeutic drug monitoring makes sure the patient receives the correct medication at the right dose within the right time frame. In order to reduce medication errors, incorporating AI systems such as Curate.AI can be used. “Leveraging AI algorithms can optimize medication dosages tailored to individual patients and predict potential adverse drug events”( Alowais, Alghamdi & Alsuhebany, 2023).
