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I chose the healthcare industry because it’s something I’ve always been interested in, especially as someone studying psychology and forensic science. Healthcare connects to every part of our lives, and the idea of using AI to improve how we diagnose and treat people is something I find both exciting and essential. I wanted to learn more about how AI is already helping patients and doctors, and what that means for the future.

I’ll explore three real-world examples of AI in healthcare: diagnostic imaging, personalized medicine, and remote patient monitoring. I’ll break down the technologies being used in each one, such as machine learning and wearable devices, and discuss how they’re making healthcare faster, more accurate, and more accessible. I’ll also address some of the challenges that accompany these changes, particularly in terms of ethics and equity.

This topic matters to me because I am passionate about improving mental and physical healthcare for everyone. The idea that AI can help reduce errors or reach people in remote areas is powerful. However, I also think it’s essential to ask questions about privacy, bias, and who’s truly benefiting. Through this project, I aim to gain a deeper understanding of both the promise and the responsibility that come with utilizing AI in such a personal and critical field.

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IBM Watson Health- AI in Diagnostic Imaging

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IBM Watson Health- AI in Diagnostic Imaging

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IBM Watson Health utilizes machine learning and natural language processing to support doctors in analyzing medical imaging, including MRIs, CT scans, and X-rays. This AI can detect patterns in scans and cross-reference them with vast amounts of medical literature and patient data to suggest possible diagnoses. Benefits include faster results, early detection, and reduced human error. However, challenges include concerns about accuracy in rare or complex cases and bias in the training data. I find this especially interesting because early diagnosis can save lives, and having a tool like this to support doctors is a smart step toward safer, more precise care.

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How IBM Watson Will Change Healthcare Forever

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Tempus utilizes artificial intelligence and machine learning to analyze clinical and genomic data, enabling doctors to create personalized cancer treatment plans. The platform processes large datasets from genetic sequencing, lab tests, and electronic health records to match patients with targeted therapies. The primary benefit is treatment precision. People can receive care tailored to their unique biology, rather than a one-size-fits-all approach. A challenge is the complexity of accurately and fairly integrating such a large amount of data. I find this fascinating because it demonstrates how AI can make healthcare feel more personalized by tailoring it to the individual, especially in life-threatening cases such as cancer.

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Medtronic utilizes AI-powered wearable devices and remote monitoring tools to track chronic conditions, such as heart disease and diabetes. These devices collect real-time data such as heart rate, glucose levels, or breathing patterns, which are analyzed using machine learning to detect abnormalities early. The benefits include better long-term disease management and fewer hospital visits. However, challenges include data privacy risks and limited access for underserved populations. I think this application is a game-changer because it helps people manage their health outside the hospital. As someone interested in mental and physical health, I see how tools like this could reduce stress for patients.

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How AI is transforming patient health monitoring

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AI in healthcare is rapidly evolving, and the future promises even more personalized, predictive, and proactive care. One major trend is the use of predictive analytics, where AI can anticipate health problems before they happen, for example, detecting early signs of stroke or cardiac arrest. We’re also seeing growth in virtual health assistants and AI-powered mental health apps that offer support 24/7. As technology advances, AI could eventually assist in surgeries or aid in triaging patients in emergency rooms.

However, as exciting as this is, the future of AI in healthcare also presents serious ethical concerns. One significant issue is algorithmic bias. If AI systems are primarily trained on data from specific populations, they may make more inaccurate predictions for others, potentially exacerbating health disparities. Another concern is privacy. Healthcare data is among the most sensitive information available, and patients must trust that it’s being used responsibly. There’s also the question of human oversight. We can’t rely on AI to replace doctors, but rather to support them.

Personally, I think it’s important to stay hopeful and curious about these advancements, but also cautious. Just because AI can do something doesn’t always mean it should. Moving forward, I believe that developers, providers, and policymakers must work together to ensure that AI is used in ways that are fair, transparent, and patient-centered.

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AI is making a major impact on healthcare and society as a whole. One of the biggest effects is on employment. While AI can help doctors and nurses by speeding up diagnoses and reducing paperwork, it also raises fears about job loss in some areas, especially administrative and support roles. At the same time, new tech-focused jobs are being created, such as data analysts or AI ethics specialists, which shift the kinds of skills the workforce needs.

Another important issue is privacy. AI systems rely on tons of patient data, and if that data isn’t protected properly, it could be misused or exposed. Patients may be hesitant to fully share their health history if they’re worried that it will not remain private. There's also a risk that AI could be used to make decisions without sufficient human input, such as denying treatment based on flawed predictions.

Equity is another major concern. If AI tools are mostly trained on data from wealthier populations, they may not work as well for marginalized communities, widening existing gaps in care. This is something I find really troubling, because healthcare should help everyone, not just the people who already have access.

I think society has to take these concerns seriously. AI has immense potential to do good, but we can’t ignore the impact it may have on people’s rights and trust. The tech is powerful, but it’s the responsibility behind it that matters.

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This project helped me understand how complex and fast-moving AI is in the healthcare field. At first, it was hard to find sources that were both current and clear, but once I focused on specific examples, the research felt more manageable. I learned how to compare different AI tools and think critically about how they're used in real life. One thing that stood out was the extent that AI is already working behind the scenes in patient care. This assignment made me more aware of both the possibilities and the risks, especially when it comes to fairness, privacy, and real-world impact.

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Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Rationale: This article provides a solid overview of AI applications in healthcare, especially diagnostic imaging, which supports the IBM Watson Health example.

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Rationale: Topol’s book gives a detailed explanation of how AI is personalizing medicine and improving patient-centered care, which connects to Tempus.

Shull, J. G. (2021). Digital health and the rise of patient monitoring: The role of AI and wearable tech. Healthcare Technology Letters, 8(3), 57–65. https://doi.org/10.1049/htl2.12014
Rationale: This source explains how Medtronic and similar companies use wearable AI tools to manage chronic conditions in real-time.

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229
Rationale: This article supports the ethics section by highlighting the need for transparency, fairness, and oversight in healthcare AI systems.

Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019). The role and limits of principles in AI ethics: Towards a focus on tensions. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 195–200. https://doi.org/10.1145/3306618.3314289
Rationale: This paper addresses fairness and conflicting values in AI development, tying into both your ethics and societal impact discussions.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Rationale: This article explains how predictive analytics is being used in clinical decision-making, which connects to your "Future Trends" post.

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510
Rationale: This journal article supports your broader explanation of how AI is changing the healthcare delivery system overall.

Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43. https://doi.org/10.1038/s41591-018-0272-7
Rationale: This article addresses privacy risks in healthcare data usage, directly supporting your points in the “Societal Impact” section.