The Convergence of Artificial Intelligence and Healthcare in Revolutionizing Diagnosis, Treatment, and Personalization: A Review

A new era in medical diagnosis, treatment, and patient care is dawning with the integration of artificial intelligence (AI) into healthcare. AI's growing proficiency in areas such as deep learning (DL), neural networks, and big data analytics is enhancing the accuracy of disease diagnoses, personalizing treatment plans, and simplifying healthcare operations. AI-driven technologies are outpacing traditional approaches in terms of speed and accuracy, from disease diagnosis via medical imaging to genetic predisposition prediction and medication discovery. In medical imaging, AI-powered algorithms have matched or even surpassed human competence in disease classification. Furthermore, genomics and proteomics benefit from AI's capacity to understand complex biological data, enabling precise therapeutic interventions. Despite these achievements, extensive AI deployment in healthcare raises ethical and technical concerns. Data privacy concerns, algorithmic bias, and the “black-box” nature of AI decision-making all raise trust and accountability concerns. Additionally, the lack of large, high-quality datasets continues to make it hard to use AI in therapeutic situations. Healthcare workers, AI researchers, and lawmakers all need to work together to solve these problems. As AI gets better, it will be combined with human skills to change the future of medicine. This will make care more personalized, effective, and open to everyone.