Ai-Driven Optimization In Healthcare: Machine Learning Models For Predictive Diagnostics And Personalized Treatment Strategies
Keywords:
AI, Healthcare, Patient care, Quality of life, Clinicians, Decision-making, Personalized treatment plans, Ethical Challenges, Bias Mitigation, Data Diversity, Fairness-aware AlgorithmsAbstract
A healthcare system is a puzzle with its problems for every participant; however, AI has impacted several fields, providing further opportunities to improve patient satisfaction and quality of life. Drawing on the current state of development, continuous growth in artificial intelligence can perform clinician functions and become part of clinical workflows, so it is vital to know AI’s function for its effective implementation in healthcare. As part of preparing for emergencies, these aspects mean healthcare providers need knowledge and tools.
This review provides a state-of-the-art summary of AI in clinical practice and discusses AI for diagnosis, treatment, and patient-related future applications. It also responds to ethical and legal issues and the necessity of human participation and involvement. In so doing, it underscores how AI can help healthcare adopt new technologies to improve their operations.
As a literature-based investigation, this study analyzed several works to determine the effects that AI has on the sphere of healthcare. AI will significantly push the horizon in diagnostics, treatment choices, and the efficiency of laboratory tests. It makes it accurate and cost-effective and reduces human interface. AI can also transform the future of personalized medicine, provide better treatment options, improve population health, and create patient engagement. Thus, severe issues regarding data protection, algorithm bias, and human supervision must be solved to effectively and safely apply AI in healthcare.
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