Abstract

This research paper endeavors to delve into the integration of Artificial Intelligence (AI), Machine Learning (ML), and big data within the healthcare industry. The study draws attention to the exponential growth of the AI market as a result of substantial investments and the emergence of AI startups throughout various regions across the globe. The study devotes attention to discussing potential opportunities in the pharmaceutical sector whereby AI-driven technologies can enhance processes related to drug discovery and development by utilizing vast datasets with intricate algorithms. Further, the paper examines the use of AI in medical diagnostics, highlighting its contribution towards improving accuracy, speed, and efficacy in disease diagnosis and treatment planning. The research underscores the significance of AI-powered protein engineering and its role in optimizing enzyme synthesis, drug design, and protein redesign through machine learning models. This study also considers the extensive consequences brought about by generative AI technology and potential unethical practices that can be used to exploit protein redesign. Lastly, the study talks about the implementation of AI in healthcare and protein engineering which brings about unavoidable ethical and regulatory challenges that cannot be ignored. Responsible development, data privacy, algorithmic transparency, and accountability are emphasized as key elements to address these challenges. Overall, the dual industry of AI and healthcare must navigate through various hurdles and seize the opportunities that lie ahead. The study illustrates how technological advancements, ethics, and gaining the public’s credence are among the factors that will determine the industry’s success in the future.

Keywords:Artificial intelligence; Healthcare; Machine learning; Protein-Engineering; Medical diagnostics; Drug discovery; Ethics

Abbreviations:AI: Artificial Intelligence; BTWC: Biological and Toxin Weapons Convention; CNN: Convolutional Neural Network; CT: Computed Tomography; CWC: Chemical Weapons Convention; ECG: Electrocardiogram EEG: Electro Encephalo Gram; EMG: Electro Myo Graphy; EHRs: Electronic Healthcare Records; LLMs: Large Language Models; ML: Machine Learning; MRI: Magnetic Resonance Imaging; PS: Protein Science; QAI: Quantum Artificial Intelligence; XAI: Explainable Artificial Intelligence