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I chose the healthcare industry because it’s one of the most exciting areas where artificial intelligence can have a measurable and immediate impact. As a student of computer science, I have a keen interest in the practical applications of machine learning models, data systems, and algorithms to healthcare issues. This Padlet examines how AI is used in three fields where code is actually helping lives: remote patient monitoring, virtual care, and diagnostic imaging.

Complex problems in healthcare necessitate scalable, data-driven solutions. AI improves decision-making and streamlines operations in the face of ineffective procedures and large patient loads. The utilization of technology I learn about in class, such as natural language processing and neural networks, to identify illnesses and suggest therapies is what most excites me. Building a model in Python is one thing, but realizing that identical models are being used in actual hospitals to forecast cardiac events or detect early cancer indications is quite another. The ethical and technical balance AI must achieve in healthcare is another thing that appeals to me. Problems like explainability, data privacy, and model bias aren't merely hypothetical; they have the power to save or destroy lives. This Padlet examines the possibilities and constraints of artificial intelligence in medicine while showcasing real-world case studies.

This endeavor is personal to me in addition to being scholarly. It examines how the concepts I'm learning as a computer science student now might influence healthcare in the future. And that encourages me to continue studying and responsibly coding.

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By analyzing X-rays, MRIs, and CT images using computer vision and deep learning, artificial intelligence is revolutionizing diagnostic imaging. For instance, Aidoc employs algorithms approved by the FDA to identify life-threatening illnesses including lung clots and brain hemorrhages. It aids radiologists in accurately and swiftly prioritizing critical patients. Convolutional neural networks that have been trained on thousands of scans are among the technologies utilized. Better triage, quicker diagnoses, and less clinician workload are among the advantages. Nevertheless, drawbacks include the dependence on high-quality imaging data and algorithm bias. I'm interested in this as a computer science student because it's a practical application of computer vision that immediately aids in clinical judgments.

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The application of AI in diagnostic Imaging.

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"CS Connect," a virtual care platform powered by AI from Cedars-Sinai, expedites patient intake and aids in early diagnosis. It collects symptoms and makes initial treatment recommendations using machine learning and natural language processing. In 77% of situations, doctors discovered that the AI's recommendations matched best practices. Reduced paperwork, quicker care, and more time for professionals to spend with patients are among the advantages. Ensuring patient privacy and data security is the primary challenge. As an NLP student, I find it amazing how real-time language analysis is enhancing medical care and making systems more intelligent and effective for both patients and providers.

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Wearable technology that gathers real-time data such as oxygen levels, heart rate, and sleep patterns is using artificial intelligence (AI) more and more in remote patient monitoring (RPM). These technologies forecast declines in patients with long-term illnesses using machine learning. HealthSnap, for instance, use AI to identify individuals who are at risk so that medical professionals may take early action. This lowers hospital admissions and ER visits. Predictive analytics and Internet of Things sensors are among the technologies used. As a student of computer science, I'm particularly curious about how federated learning and edge computing allow for safe, low-latency health insights in resource-constrained real-world settings.

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Use of AI and Machine Learning in Remote Patient Monitoring (RPM)

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Future Trends and Ethical Considerations 

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Future Trends and Ethical Considerations 

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Over the next five years, AI in healthcare is anticipated to expand quickly. Clinical documentation will be automated by generative AI technologies, relieving physicians of some of their administrative duties. Additionally, AI will be used more in precision medicine to assist customize therapies based on lifestyle and genetic information. Integration with electronic health records will become crucial as more institutions use AI platforms.

But with this development come moral obligations. Inequitable treatment may result from bias in training data. An AI model that was mostly trained on data from white patients, for instance, might not function well for patients of color. Designing algorithms with equity and diversity in mind is essential. Another big worry is data privacy, particularly when using virtual assistants and remote monitoring tools. Patients need to have faith that their private data is safe.

The over-reliance on AI is another issue. Errors may occur if medical professionals follow AI instructions mindlessly and without question. The efficiency of machines and human judgment must be balanced. Transparency in the process of AI decision-making must be guaranteed by regulations. I see this as a call to action as a student of computer science. Developers like me are responsible for creating auditable, transparent systems that put privacy and equity first. While I'm hopeful about AI's possibilities, I'm also realistic about the obligations it lays on humans. We must handle code carefully because it has an impact on people's lives, particularly in the healthcare industry.

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AI is transforming healthcare in ways that go beyond medical facilities. One significant effect is on employment. AI can automate processes like image analysis and data entry, but it also generates new jobs like data auditors and AI ethicists. Although job loss is a risk, particularly in administrative positions, the overall impact can be a change in job duties rather than termination. AI can also make healthcare more accessible. Virtual AI assistants, for instance, can help patients in remote locations who don't often have access to medical professionals. This, however, makes the assumption that patients are digitally literate and have internet access, which isn't always the case. AI carries the potential of exacerbating already-existing health inequities if it is not applied carefully.

Another major concern is privacy. Protecting patient data is essential as AI systems gather more of it. Misuse or breaches of data could make people less trusting of healthcare institutions. HIPAA and other regulations need to change to handle the risks associated with AI.  There is also equity at risk. Global healthcare disparities may worsen if AI systems are restricted to affluent hospitals or industrialized nations. However, when used carefully, AI can help level the playing field by providing underprivileged areas with top-notch monitoring tools and diagnostics.

As a student of computer science, I believe that this is the point at where ethics and technology meet. Who gains from these instruments is a question we must examine, and w ho is excluded? AI has the potential to advance society, in my opinion, but only if it is developed with equity, privacy, and inclusivity in mind.

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I now have a better understanding of the practical applications of computer science in healthcare thanks to my research on this subject. Through reading case studies and technical papers, I was able to overcome one of my challenges: comprehending the clinical context of AI applications. It opened my eyes to see how the technologies I study, such as natural language processing and neural networks, are enhancing people's lives in medical situations. This project made me realize that producing amazing code isn't enough; it also needs to be trustworthy, moral, and people-focused. AI in healthcare is now one of the most significant fields where a profession in computer science may have an impact, in my opinion.

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Aidoc. (2025). AI in diagnostic imaging. https://www.aidoc.com
Rationale: Chosen for its real-world use of deep learning and CNNs in medical imaging, showing how AI directly supports clinical workflows.

Business Insider. (2025, July). Cedars-Sinai’s AI platform transforms care delivery. https://www.businessinsider.com/cedars-sinai-la-healthcare-organization-ai-platform-patient-care-treatment-2025-7
Rationale: Provides a strong case study on NLP in healthcare and how AI streamlines virtual patient intake.

HealthSnap. (2024). AI in remote patient monitoring. https://healthsnap.io
Rationale: Offers insight into real-time data collection via wearables, which connects directly to concepts like federated learning and IoT.

Shaik, T., & Tao, X. (2023). Remote patient monitoring using AI. arXiv. https://arxiv.org/abs/2301.10009
Rationale: A technical preprint that explains underlying machine learning models and edge AI frameworks used in RPM.

Time. (2025). AI prevents medical errors in clinics. https://time.com/7304457/ai-prevents-medical-errors-clinics
Rationale: Highlights the societal benefit of AI improving care access and reducing diagnostic errors in underserved areas.

NVIDIA. (2025). State of AI in Healthcare 2025 Survey Report. https://www.nvidia.com/en-us/lp/industries/healthcare-life-sciences/ai-survey-report
Rationale: Offers statistical insights and trends in healthcare AI adoption, useful for understanding future developments.

LitsLink. (2025). AI in healthcare: Breaking down statistics and trends. https://litslink.com/blog/ai-in-healthcare-breaking-down-statistics-and-trends
Rationale: Used to support future trends section with up-to-date data and real examples of AI application.