I chose to explore AI in the healthcare industry because Healthcare affects everyone, and AI in this field can mean faster diagnoses, better treatments, and even saved lives. With AI becoming a norm for tools like wearable health trackers, and more advanced tools like diagnostic imaging and robotic surgeries, I was curious to see how it’s changing the way doctors and hospitals operate.

In this Padlet, I will highlight three specific AI applications in healthcare, diagnostic imaging, personalized medicine, and patient monitoring through wearables. I’ll explain how technologies like machine learning, natural language processing, and computer vision are helping professionals in the medical field

This topic matters to me because I believe healthcare should be accessible, fair, and accurate for everyone. AI has the potential to solve some major issues in healthcare, but it also raises concerns such as privacy, bias in algorithms, and job displacement.

Personal Insight:

I think it’s amazing that AI can help doctors detect serious conditions earlier. Knowing that technology like this could help save lives or prevent misdiagnoses but it still think it needs more training and collecting more data

Related video:

https://www.youtube.com/watch?v=3DUyzPvsMQ8&ab_channel=SiemensHealthineers

https://www.researchgate.net/figure/Pathway-of-artificial-intelligence-in-medical-imaging_fig2_354697473

However, these advancements bring serious ethical concerns. One of the most significant issues is data privacy. AI requires large amounts of sensitive medical information, and there’s always a risk that data could be misused or poorly protected. Another concern is bias in AI models. If algorithms are trained on non-diverse datasets, they may perform worse for patients, which can lead to misdiagnosis.

According to Dankwa-Mullan (2024), promoting equity and patient trust is critical to preventing AI from reinforcing existing disparities in care. Weiner et al. (2024) also stress the need for regulations and accountability in clinical AI systems.

Personally, I still think we need time for AI to be used in the medical field. It is cool how the stuff we saw in movies or sci-fi is coming to life, but there is still risk to people relying on AI, especially with people’s lives on the line. This is just my opinion. But I believe it should be used responsibly and fairly; it just needs time.

1. Dankwa-Mullan, I. (2024). Health equity and ethical considerations in using artificial intelligence in public health and medicine. Preventing Chronic Disease, 21(240245).

   https://www.cdc.gov/pcd/issues/2024/24_0245.htm

2. Weiner, E. B., Dankwa‑Mullan, I., Nelson, W. A., & Hassanpour, S. (2024). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.

   https://arxiv.org/abs/2412.03576

But it’s not all good news. One thing I worry about is jobs. As AI starts doing more paperwork and administrative tasks—like billing or taking notes during appointments some people could lose their jobs or have to completely change what they do. A report from HIMSS talks about how workers will need to adapt or re-skill to keep up with all these changes (HIMSS, 2024).

There’s also the issue of fairness. If the AI is trained mostly on data from one type of population, it might not work as well for everyone. That means some groups like Black or hispanic patient could get less accurate diagnoses or treatment. Researchers at Rutgers have pointed out how bias in medical AI can cause real harm if we’re not careful (Stetler, 2024).

AI will take over more jobs in the near future. Machines already took over factory workers and assembly-line workers, with robots that can do their jobs in half the time a human can. I like the idea of AI helping but with this people will lose job and won't be able to support their own family. It

scary to think that AI will take jobs and it feels like the rich will continue to be richer and the poor will stay poor as the people who is able to make these machines are the ones who can fund and have money. And as for the people who say coding is also something the poor can do, the field of coders is overwhelmed welming with everyone trying to be the best

1. HIMSS. (2024). The impact of AI on the healthcare workforce: Balancing opportunities and challenges. Healthcare Information and Management Systems Society.

https://legacy.himss.org/resources/impact-ai-healthcare-workforce-balancing-opportunities-and-challenges

2. Stetler, C. (2024). AI algorithms used in healthcare can perpetuate bias. Rutgers University–Newark News.

https://www.newark.rutgers.edu/news/ai-algorithms-used-healthcare-can-perpetuate-bias

1. HIMSS. (2024). The impact of AI on the healthcare workforce: Balancing opportunities and challenges. Healthcare Information and Management Systems Society.

   https://legacy.himss.org/resources/impact-ai-healthcare-workforce-balancing-opportunities-and-challenges

   Rationale: This report explains how AI is reshaping healthcare jobs and emphasizes the need to prepare the workforce for changing roles.

2. Stetler, C. (2024). AI algorithms used in healthcare can perpetuate bias. Rutgers University–Newark News. https://www.newark.rutgers.edu/news/ai-algorithms-used-healthcare-can-perpetuate-bias

   Rationale: This article highlights how racial and demographic bias can appear in medical AI tools due to flawed training data.

3. Dankwa-Mullan, I. (2024). Health equity and ethical considerations in using artificial intelligence in public health and medicine. Preventing Chronic Disease, 21(240245).

   https://www.cdc.gov/pcd/issues/2024/24_0245.htm

   Rationale: Offers insight into the ethical concerns and equity issues involved in using AI in healthcare settings.

4. Weiner, E. B., Dankwa‑Mullan, I., Nelson, W. A., & Hassanpour, S. (2024). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.

   https://arxiv.org/abs/2412.03576

   Rationale: Explores strategies for ensuring transparency, fairness, and accountability in clinical AI tools.

5. Topol, E. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44–56.

   https://www.nature.com/articles/s41591-018-0300-7

   Rationale: A foundational article showing how AI is transforming diagnosis and decision-making in modern healthcare.

6. ResearchGate. (2021). Pathway of artificial intelligence in medical imaging.

   https://www.researchgate.net/figure/Pathway-of-artificial-intelligence-in-medical-imaging_fig2_354697473

   Rationale: This diagram provides a visual overview of how AI integrates into the medical imaging process from input to outcome.