Post 7: Artificial Intelligence in Healthcare

The rapid advancement of information technology has redefined how industries operate and serve their customers. Among the most transformative developments is the integration of artificial intelligence into healthcare. This final project explores AI in healthcare and its deep connection with the core principles of information technology, including computer hardware, software, programming languages, databases, and networking. It demonstrates how AI can enhance patient outcomes and operational efficiency while highlighting the significance of computer literacy for professionals in this evolving landscape. The purpose of this paper is to showcase how AI in healthcare exemplifies the key components of information technology while serving as a foundation for future learning and innovation in the field.

AI connects to foundational IT concepts by utilizing basic computing processes, including data input, binary logic, and program execution. Programming languages like Python and Java are used to design AI algorithms that can analyze imaging scans, detect anomalies, and even suggest treatment options based on data trends. These operations rely on major hardware components such as processors, memory, and storage to handle massive datasets efficiently. Without high performance computing systems, real time AI driven diagnostics would not be feasible.

AI also depends on application software and databases. Tools like IBM Watson Health and Google Health AI integrate with electronic health records (EHRs) to deliver insights and decision support. These applications require robust database management systems to store, retrieve, and process medical data securely and accurately. Structured Query Language (SQL) and NoSQL databases are critical for handling the structured and unstructured data generated in healthcare settings. This illustrates how AI relies on both relational and non relational data models to function effectively.

AI in healthcare uses different programming languages and execution methods based on the task. Python is popular for training machine learning models, while R and Java are also used for statistical modeling and system integration. AI applications typically run on operating systems like Linux or cloud based platforms using containers and virtual machines for scalability and performance. These execution methods are vital for deploying AI in real time hospital environments and research.

Networking plays a pivotal role in the success of AI tools. Data is often shared across hospitals and cloud platforms, requiring secure and reliable connections. Network protocols and cybersecurity measures are essential to protect sensitive health information. Professionals in the field must understand these concepts to deploy and maintain secure AI systems. This also includes the management of private networks, VPNs, and firewalls to comply with HIPAA and protect patient confidentiality.


References:

Jiang, F., Jiang, Y., Zhi, H., et al. (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

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

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