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My love for medicine and my fascination with the ways that technology is changing patient care are the main reasons I chose the healthcare industry. I'm interested in how artificial intelligence (AI) improves diagnosis, treatment, and communication within healthcare systems by bridging the gap between data and human compassion. I'm a health science and nursing student. AI has a huge impact on how patients receive care as well as how doctors operate.

Through this Padlet, I will explore three main applications of AI in healthcare: disease diagnosis, medical image analysis, and AI-powered virtual assistants. Each post will describe the current applications of artificial intelligence (AI) technology, including machine learning, computer vision, and natural language processing, as well as its advantages and disadvantages. To demonstrate AI's practical effects, I will also provide key data, visual examples, and observations from current research.

The potential of AI to improve healthcare's efficiency, equity, and personalization is what most interests me about this subject. AI enables healthcare professionals to concentrate more on the human side of medicine: listening, empathizing, and connecting, by detecting diseases earlier and relieving administrative demands. As someone who appreciates both creativity and empathy, I think it's critical for the upcoming generation of professionals to comprehend how AI is affecting healthcare. In addition to broadening my understanding of contemporary medicine, this investigation motivates me to think about how I might use technology to support patient-centered care in my own future profession.

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Image of digital health dashboard- use of AI to identify early stages of diseases.

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1. Artificial Intelligence improves accuracy and saves lives by using machine learning (ML) to evaluate medical data and identify illnesses early. Compared to conventional testing, DeepMind Health and similar systems can detect cardiac disorders or cancer more quickly. While Krishnamoorthy et al. (2024) warn the readers about off-label use, data bias, and privacy concerns, Jimma (2023) highlights how AI-driven diagnostics reduce workload and human error. This intrigues me since AI enables healthcare professionals to reach patients more quickly and customize treatment. Seeing technology improve accuracy while reminding aspiring medical professionals like me of their moral obligation to maintain equity and patient confidence in AI systems is encouraging.

Relevant video:

https://youtu.be/NWSnVn-WljQ?si=_a2oUg_zVzR8rIn4

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3D visual of organs- showing precision in medical imaging.

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By applying machine learning to analyze medical data and detect diseases early, artificial intelligence (AI) increases accuracy and saves lives. DeepMind Health and similar systems can identify cancer or heart problems faster than traditional testing. Jimma (2023) emphasizes how AI-driven diagnostics lessen workload and human error, while Krishnamoorthy et al. (2024) caution about off-label use, data bias, and privacy issues. I'm interested in this because AI allows medical practitioners to reach patients faster and provide more individualized care. It's encouraging to see technology advance accuracy while serving as a reminder to me and other aspiring medical professionals of our moral responsibility to uphold equity and patient trust in AI systems.

Relevant Video:

https://youtube.com/shorts/hI_YhuXLl7c?si=DWQTQyvZLjJtNm6R

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AI Powered Virtual Assistants for Patient Communication  

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AI Powered Virtual Assistants for Patient Communication  

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By applying machine learning to analyze medical data and detect diseases early, artificial intelligence (AI) increases accuracy and saves lives. DeepMind Health and related systems can identify heart problems or cancer faster than traditional testing. Jimma (2023) emphasizes how AI-driven diagnostics lessen workload and human error, while Krishnamoorthy et al. (2024) caution about off-label use, data bias, and privacy issues. I'm interested in this because AI helps physicians reach patients faster and provide more personalized care. It is very encouraging to see technology improving accuracy, even though I and other aspiring medical professionals are reminded of our moral duty to uphold equity and patient trust in AI systems.By applying machine learning to analyze medical data and detect diseases early, artificial intelligence (AI) increases accuracy and saves lives. DeepMind Health and related systems can identify heart problems or cancer faster than traditional testing. Jimma (2023) emphasizes how AI-driven diagnostics lessen workload and human error, while Krishnamoorthy et al. (2024) caution about off-label use, data bias, and privacy issues. I'm interested in this because AI helps physicians reach patients faster and provide more personalized care. It is very encouraging to see technology improving accuracy, even though I and other aspiring medical professionals are reminded of our moral duty to uphold equity and patient trust in AI systems.

Relevant video:

https://youtu.be/FldnZIdJJ9U?si=n3DCcVSqxzGGmhTT

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Future Trends and Ethical Considerations 

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Future Trends and Ethical Considerations 

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Artificial intelligence (AI) in healthcare is developing quickly, and new developments could enhance diagnosis, treatment, and preventative care. Predictive analytics is one significant development that enables physicians to foresee illnesses before symptoms appear. AI-assisted robotic surgery is another revolutionary development, where algorithms improve surgical accuracy and shorten recovery periods. Such advancements are fostering a cooperative relationship between humans and machines, enabling medical professionals to make quicker, evidence-based decisions while retaining clinical empathy, according to the MDPI (2024) report on Artificial Intelligence in Healthcare. In contemporary medicine, the concept of high-quality care is being redefined by this fusion of compassion and data.

Even with these encouraging advancements, moral dilemmas continue to be a major worry. According to Sung and Stewart (2024), artificial intelligence (AI) has the potential to increase healthcare accessibility and accuracy, but if algorithms are trained on biased or insufficient datasets, it may also reinforce inequality. In order to avoid harm and maintain patient trust, their analysis emphasizes the necessity of openness, justice, and accountability. This strikes me as extremely pertinent since, if technology develops without ethical supervision, the fundamental tenets of healthcare: confidentiality, equity, and empathy- may be jeopardized.

In my opinion, the most sustainable course of action is the ethical integration of AI, where responsibility and innovation are balanced. As a healthcare enthusiast, I am motivated by AI's potential to bridge gaps in the provision of care, but I am also steadfast in my commitment to promoting its moral, inclusive, and human-centered use.

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In addition to changing clinical practice, the emergence of artificial intelligence (AI) in healthcare is also changing society at large. While AI is improving patient outcomes, predicting disease outbreaks, and improving diagnostic accuracy, these developments also have complicated societal ramifications. Employment is one of the main issues. Some healthcare workers worry about losing their jobs as automated systems get better at tasks like image analysis and administrative documentation. But according to Jimma (2023), AI is redefining roles rather than replacing people, pushing professionals to concentrate more on complex decision-making, empathy, and communication—skills that machines cannot match.

Another urgent concern is privacy. Large volumes of private health data are used by AI, which raises concerns about data ownership and consent. Sung and Stewart (2024) stress that in the absence of stringent ethical standards, the improper use of personal information may damage patient confidence and deter people from seeking medical attention. Equity is still a major obstacle, too. Biased algorithms and unequal access to AI tools could exacerbate already-existing disparities, especially among marginalized populations, according to Krishnamoorthy et al. (2024).

Personally, I see AI as a tool that needs to be handled carefully rather than as a perfect solution because of these societal effects. As a healthcare enthusiast, I believe AI has the potential to increase access and decrease errors, only if it is applied with equity, inclusivity, and transparency in mind. The difficulty is in making sure that as technology advances, it benefits communities rather than causing gaps in opportunity and health to widen.

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I was intimidated by the likelihood of using AI in healthcare before I started this study. I used to think that doctors, nurses, and other healthcare professionals would eventually be replaced by AI, eliminating the human element that gives medicine its purpose. But as I read academic papers and observed real-world applications, I came to understand that AI enhances humankind rather than replaces it. Clinicians can concentrate more on empathy and direct care while it helps lower diagnostic errors, develops individualized treatment plans, and enhances patient monitoring.

One of the main challenges I faced was understanding the complex scientific language used in many research studies. I overcame this by breaking down terms, cross-referencing information, and focusing on how each concept connects to patient care. Through this process, my perspective completely changed. I now see AI as a collaborative tool- one that combines data, compassion, and innovation to make healthcare more efficient, equitable, and human-centered than ever before.

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Artificial Intelligence in Healthcare: Current State and Future Perspectives. (2024). MDPI – Multidisciplinary Digital Publishing Institute. https://mdpi-res.com/bookfiles/book/9229/Artificial_Intelligence_in_Healthcare_Current_State_and_Future_Perspectives.pdf?v=1760231380

ETHOS Treatment. (n.d.). Image from “Telehealth therapy 101: How online sessions work and what to expect.”Retrieved October 12, 2025, from https://www.ethostreatment.com/telehealth-therapy-101/

Jimma, B. L. (2023). Artificial intelligence in healthcare: A bibliometric analysis. Telematics and Informatics Reports, 9,Article 100041. https://doi.org/10.1016/j.teler.2023.100041

Krishnamoorthy, M., Sjoding, M. W., & Wiens, J. (2024). Off-label use of artificial intelligence models in healthcare. Nature Medicine, 30(6), 1525–1527. https://doi.org/10.1038/s41591-024-02870-6

Miller, A. (2025). How digital hospital dashboards are revolutionizing medical decision-making. Multipurpose Themes. https://multipurposethemes.com/blog/how-digital-hospital-dashboard-are-revolutionizing-medical-decision-making/

Smith, J. (2024). Sunset over the lake [Image]. https://share.google/images/M03MmoN5NZ3bv5qE0

Sung, J. J. Y., & Stewart, C. (2024). Artificial intelligence in medicine: From ethical, social, and legal perspectives (1st ed.). Academic Press. https://doi.org/10.1016/C2021-0-02164-7

Telehealth Nursing Video. (2025). [Video]. YouTube. https://youtu.be/FldnZIdJJ9U?si=n3DCcVSqxzGGmhTT

Vithlani, J., Hawksworth, C., Elvidge, J., Ayiku, L., & Dawoud, D. (2023). Economic evaluations of artificial intelligence-based healthcare interventions: A systematic literature review of best practices in their conduct and reporting. Frontiers in Pharmacology, 14. https://doi.org/10.3389/fphar.2023.1220950

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tbd