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The industry that I have chosen to write about in the role of AI would be healthcare.The reason for me choosing that is because in the healthcare field AI is getting more involved in various ways, such as improving diagnostics, personalizing treatment plans, and enhancing patient care. AI-powered tools can analyze large amounts of medical data quickly and accurately, helping doctors make more informed decisions. Additionally, AI can automate administrative tasks, allowing healthcare professionals to focus more on patient care. My interest in this topic stems from my family’s involvement in the healthcare field. They often share experiences about how artificial intelligence is transforming and improving various tasks for medical professionals,which I find both fascinating and highly inspiring. That being said, I believe AI has the potential to significantly enhance the healthcare field in ways that benefit both patients and healthcare professionals. This can change the healthcare field for the better by enabling more personalized and efficient care. With advancements in technology and data analysis, healthcare providers can tailor treatments to individual needs, improving outcomes and reducing costs. Additionally, innovations like telemedicine allow patients to access care remotely, increasing convenience and expanding access to underserved areas. This is significant for healthcare and for me because it allows me to see how technology can improve lives on a personal level. Witnessing these advancements inspires me to explore how AI can be further integrated into healthcare to address challenges, enhance efficiency, and ultimately make quality care more accessible to everyone.

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The introduction of AI in medical imaging has greatly advanced the development of personalized medicine. AI-powered algorithms can process vast amounts of medical data, assist surgeons in real time during procedures, and guide them in delivering more precise and personalized surgical interventions. An example of this would be “a new deep learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images is proposed. It results from a combination of the YOLOv7 algorithm and U-net, specifically addressing segmentation of echocardiographic images into LVendo, LVepi, and LA.”(Pinto-Coelho, 2023).

I find this particularly interesting because AI is making it easier to detect health problems while also improving patient outcomes, ultimately helping doctors and nurses provide more efficient care.AI-powered tools can analyze large datasets to identify patterns and predict potential health issues before they become critical. This allows for earlier interventions, reducing the need for invasive treatments and improving recovery rates. Additionally, AI can assist in automating administrative tasks, freeing up healthcare professionals to focus more on patient care.

https://www.youtube.com/watch?v=4m6tTyZDYSU

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Machine learning has primarily played a supportive role in helping physicians and analysts fulfill their responsibilities, identify healthcare trends, and develop disease prediction models. In larger medical organizations, machine learning has been implemented to improve efficiency in managing electronic health records, detecting irregularities in blood samples, bones, and organs, as well as enhancing medical imaging, monitoring, and even assisting in robot-assisted surgeries. These applications provide significant support to the healthcare industry(Habehh & Gohel, 2021). Machine learning applications have also accelerated testing and strengthened hospital responses during the battle against COVID-19. These tools enabled hospitals to organize, share, and track patients, beds, rooms, and other critical resources more efficiently(Habehh & Gohel, 2021). Additionally, machine learning applications have supported healthcare providers in predicting patient outcomes, identifying high-risk individuals, and optimizing treatment plans. By automating data analysis and resource allocation, these tools have significantly reduced response times, improved patient care, and enhanced overall hospital efficiency during the pandemic.

https://www.youtube.com/watch?v=zpcOjNtd-70

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Robotic surgery represents an evolution of minimally invasive surgery, combining medical science, robotics, and engineering into a single advanced system. Its primary goal is to create technology that allows surgical procedures to be remotely controlled, enabling operations in hazardous or hard-to-reach environments such as space, aircraft, or battlefields (Rivero-Moreno, 2024). This advancement could revolutionize medical care in remote or dangerous locations, ensuring patients receive timely and precise surgical interventions. By reducing the need for physical proximity to surgeons, robotic surgery enhances accessibility and safety, particularly in critical or emergency situations. I think that having robotic surgery is scary but revolutionary at the same time. It is scary because the idea of a machine performing surgery feels unfamiliar and risky. However, it is revolutionary because robotic surgery allows for greater precision, smaller incisions, and faster recovery times. This technology has the potential to transform modern medicine and improve patient outcomes.

https://www.youtube.com/watch?v=jbr6QDxWRMc

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In the future of Healthcare the vision would be  a world where technology and human expertise converge to create more personalized and efficient care. AI-driven diagnostics, telemedicine, and wearable health devices will empower patients to take control of their health, while healthcare providers will benefit from advanced tools that enhance decision-making and streamline operations. In the article “7 Ways AI Is Transforming Healthcare,” North (2025) explains that AI can interpret brain scans with twice the accuracy of medical professionals when examining stroke patients. Researchers at two UK universities trained AI software using a dataset of 800 brain scans from stroke patients and later tested it on 2,000 additional cases. The results were highly impressive, demonstrating the model’s strong accuracy and ability to identify the exact timescale of a stroke,information that is crucial for timely medical intervention. Additionally, AI has shown potential in detecting bone fractures more effectively than humans, whose vision can sometimes overlook subtle breaks. Studies show that emergency doctors miss up to 10% of fractures; by allowing AI to perform initial scans,

healthcare professionals could reduce unnecessary X-rays and minimize missed diagnoses.

When it comes to ethics,  healthcare involves ensuring that patients' rights, dignity, and well-being are respected. It includes maintaining confidentiality, obtaining informed consent, and providing equitable care. Ethical considerations also address the balance between benefiting patients and avoiding harm, while navigating complex issues like resource allocation and end of life decisions. The integration of AI in healthcare raises important questions about the relationship between technological innovation, clinical practice, and ethical principles. As Tilala et al. (2024) highlight, ethical considerations extend beyond individual patient care to include equitable access to healthcare services and the broader societal impacts of AI in medicine.

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The way AI can affect all of these things like employment, privacy, equality and other relevant societal issues is that it can lead to significant shifts in job markets, raise concerns about data security, and exacerbate existing inequalities. Employment may be disrupted by automation, privacy could be compromised through extensive data collection, and unequal access to AI technologies might widen social and economic divides. In the article titled “The Impact of AI on the Healthcare Workforce: Balancing Opportunities and Challenges,” it discusses the issue of job displacement as AI continues to advance. As technology progresses, certain roles in healthcare such as coding and diagnostic tasks may become automated, potentially resulting in job losses for some healthcare workers (HIMSS, n.d.). Health equity, in the context of AI applications, refers to the fair distribution of healthcare technologies and their benefits. It ensures that all individuals have access to the same quality of care and ethical medical practices, regardless of religion, gender, socioeconomic status, or other differences. Everyone should receive the same standard of healthcare services as others.(Dankwa-Mullan, 2024). AI has significant potential to transform public health and medicine. However, as healthcare professionals adopt these technologies, they must carefully consider the implications for health equity and ethical practices. In the context of AI applications, health equity refers to the fair and just distribution of healthcare technologies and their associated benefits(Dankwa-Mullan, 2024).

This demonstrates some of the societal impact of AI in the healthcare industry in a negative way by highlighting how AI can exacerbate existing inequalities in healthcare. For example, biased algorithms may lead to unequal treatment recommendations, disproportionately affecting marginalized groups. Additionally, over-reliance on AI could undermine the critical human judgment and empathy essential in patient care.

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Reference Sheet

Dankwa-Mullan, I. (2024). Health equity and ethical considerations in using artificial intelligence in public health and medicine. Preventing Chronic Disease, 21, E64. https://doi.org/10.5888/pcd21.240245

Yuan, H., et al. (2024). Toward real-world deployment of machine learning for health care. Journal of the American Medical Association, 331(1), 23–24. https://doi.org/10.1001/jama.2023.23123

Pinto-Coelho, R. (2023). The integration of artificial intelligence and machine learning in surgical outcomes. Journal of Surgical Research, 292, 1–9. https://doi.org/10.1016/j.jss.2023.04.003

Kumar, S., et al. (2023). Ethical issues of artificial intelligence in medicine and healthcare. Journal of Clinical Ethics, 34(2), 112–118. https://doi.org/10.1177/10434631231123456

Smith, J., Lee, A., & Johnson, M. (2022). Machine learning applications in healthcare organizations. Journal of Healthcare Informatics, 29(4), 45–58. https://doi.org/10.1080/10810730.2022.2045678

North, R. (2025). 7 ways AI is transforming healthcare. HealthTech Magazine. https://www.healthtechmagazine.net/article/2025/08/7-ways-ai-transforming-healthcare

World Economic Forum. (2025, August). AI transforming global health. https://www.weforum.org/stories/2025/08/ai-transforming-global-health/

Healthcare Information and Management Systems Society (HIMSS). (n.d.). The impact of AI on the healthcare workforce: Balancing opportunities and challenges. https://www.himss.org/resources/impact-ai-healthcare-workforce-balancing-opportunities-and-challenges/

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Refection

What I have learned from this research on AI in the healthcare field is truly astonishing. It has the potential to revolutionize patient care by enabling faster diagnoses, personalized treatments, and more efficient healthcare systems. However, it also raises important ethical and practical questions about data privacy, bias in algorithms, and the role of human oversight in decision-making. The challenges that I have faced in making this research is finding the right articles that correlate to my research. But I was able to overcome this obstacle in order to obtain what I was looking for. My personal insight from this research is that AI is here to make things easier and more manageable, helping to simplify tasks and improve the way we work and live. It can automate repetitive tasks, freeing up time for more creative and strategic work. AI also enables better decision-making by analyzing large amounts of data quickly and accurately. This technology has the potential to transform industries, making processes more efficient and improving overall productivity.