Novelties in additive manufacturing and bio-printing

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Chidera Stanley A

Nabafat AI, United States

: J Comput Eng Inf Technol

Abstract

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Machine Learning in Healthcare: Advancements, Challenges, and Ethical Considerations Machine learning (ML) has emerged as a transformative force in the healthcare industry, offering the potential to revolutionize patient care, disease diagnosis, treatment planning, and resource allocation. This abstract provides a comprehensive overview of the current landscape of machine learning applications in healthcare, highlighting the advancements, challenges, and ethical considerations that shape this dynamic field. Over the past years, machine learning has demonstrated remarkable achievements in healthcare. ML algorithms, such as deep learning and ensemble methods, have shown remarkable accuracy in image analysis tasks like medical imaging interpretation, enabling early detection of diseases like cancer, diabetic retinopathy, and cardiovascular anomalies. Natural language processing (NLP) techniques have empowered the analysis of vast amounts of clinical text, aiding in electronic health record (EHR) mining, clinical documentation, and decision support. ML models have also been pivotal in predicting patient outcomes, optimizing treatment regimens, and identifying potential outbreaks by analyzing health data patterns. Machine learning has already made significant inroads in healthcare, holding the promise to transform patient care and medical research. The successes achieved thus far are encouraging, but there is a pressing need to address the challenges and ethical considerations associated with its implementation. Collaboration between machine learning engineers, healthcare professionals, ethicists, and policymakers is essential to harness the potential of ML while upholding patient rights, data security, and healthcare equity. As the field continues to evolve, the integration of machine learning in healthcare stands as a testament to human ingenuity and its potential to reshape the future of medicine. Recent Publications: 1. How to build a movie recommendation system using PyTorch and collaborative filtering.

Biography

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Chidera Stanley Agwu is a skilled AI and Machine Learning professional with a BSc in Mechanical Engineering. He excels in deep learning, computer vision, and object detection, having contributed to projects for companies like Nabafat.AI and Roc4Tech. His expertise includes creating accurate image captioning and bird species recognition models, fine-tuning deep learning algorithms, and improving object detection accuracy. Chidera's proficiency extends to languages like Python and tools like PyTorch, TensorFlow, and OpenCV. He is known for his dedication, innovation, and commitment to excellence in the field. Connect with him on LinkedIn or explore his work on GitHub and his personal websit.