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.

Relevant video:

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

Relevant Video:

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

Relevant video:

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

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.

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.

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.

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