Astha Shrestha

  The Role of Artificial Intelligence and Machine Learning in Healthcare Abstract Artificial Intelligence (AI) and Machine Learning (ML) are reshaping healthcare by enhancing diagnostic accuracy, personalizing treatments, and optimizing operations. This report examines their applications, challenges, and future potential, emphasizing Explainable AI (EXAI) for ethical and transparent implementations. A critical analysis of competing technologies, cost-benefit trade-offs, and technical considerations is included, supported by rigorous citations. 1. Introduction The integration of AI and ML into healthcare represents a transformative shift from manual, intuition-driven practices to data-driven decision-making. AI, encompassing systems that mimic human intelligence, and ML, enabling systems to learn from data, are core technologies in Healthcare 4.0, a paradigm that leverages big data, IoT, and smart systems. The progression toward Healthcare 5.0 builds on this foundation, emphasizing ...

Data Science vs Artificial Intelligence: Understanding the Differences and Overlaps In the era of digital transformation, the terms Data Science and Artificial Intelligence (AI) often dominate conversations. While both fields are integral to extracting value from data, they serve different purposes and have distinct goals, methods, and applications. This blog explores the nuances between these two domains, shedding light on their similarities, key differences, and how they shape the future of technology. History of Data Science and Artificial Intelligence Data Science: The roots of data science can be traced back to the early 1960s when John Tukey introduced the term "data analysis." With the advent of computers, the ability to store and process large datasets became feasible, leading to the evolution of data-driven decision-making. In the 2000s, data science emerged as a distinct discipline, driven by the rise of big data and advancements in statistical and computational tec...

Data Analyst vs. Data Scientist In today’s world, data is everywhere! Two important roles that help make sense of all this data are data analysts and data scientists. While they both work with data, their jobs are quite different. Let’s break it down in a way that’s easy to understand. What They Do Data Analyst: Think of a data analyst as a detective for numbers. They look at past data to find patterns and trends. Their main job is to answer questions like, “What happened last month?” or “How many people bought this product?” They use tools like Excel and SQL to create reports and visuals that help businesses make decisions. Data Scientist: A data scientist is more like a wizard with data! They not only look at what has happened but also try to predict what will happen next. For example, they might use machine learning to forecast sales for the next quarter. Data scientists work with both structured data (like tables) and unstructured data (like text or images) and use programming la...