Abstract
The amalgamation of artificial intelligence (AI) and machine learning (ML) in healthcare offers a revolutionary prospect to improve patient outcomes, optimize workflow, and curtail expenses. Robust computational resources, competent interdisciplinary teams, and careful management of high-quality, diverse datasets are necessary for the successful deployment of ML models. To preserve public confidence and guarantee adherence to legal requirements, ethical issues, specifically those pertaining to patient consent, data privacy, and confidentiality are crucial. Regulatory bodies, legislators, and healthcare providers must all be involved in stakeholder engagement to establish a supportive environment for ML integration. The research highlights the significance of transparency and interpretability, promoting explainable AI models that augment clinician trust and enable wider adoption in the medical community. This thorough analysis finds important gaps in the current state of ML applications in healthcare and highlights new trends. It discusses ethical ramifications, realistic implementation guidelines, and the need for cooperative stakeholder engagement. The goal of the paper is to close the “translation gap” between model development and clinical application by examining real-world applications and offering a framework for the methodical integration of ML in clinical settings. The study promotes models that are not only technically sound but also user-friendly and in line with the practical needs of healthcare professionals by emphasizing the human-centric design of ML systems. By facilitating accurate and timely diagnoses, optimizing treatment plans, and eventually raising the standard of healthcare services as a whole, the strategic application of ML in the medical field holds the potential to completely transform patient care. This study opens the door to a future in which AI-driven healthcare solutions are smoothly integrated into routine clinical practice by providing useful insights and guidelines to enhance trustworthy and moral integration.
Published Version
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