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Abstract
Introduction: The use application of artificial intelligence (AI) in healthcare has changed dramatically since its early exploration in diagnosing acute abdominal pain. Today, AI enhances clinical decision-making, precision medicine, and diagnostics, particularly in visually-focused specialties like radiology and dermatology. Despite its potential, widespread adoption is hindered by concerns over transparency, especially with black-box models. Explainable AI aims to address this by improving the transparency and traceability of complex machine learning models, thereby maintaining patient trust and supporting evidence-based decision-making.
Research significance: This research is significant as it explores the way that artificial intelligence (AI) is changing medical practice, emphasizing explainable AI to enhance transparency and trust. By addressing challenges in complex clinical decision-making and advancing precision medicine, this study contributes to improved diagnostics and treatment. Additionally, it examines the ethical, educational, and regulatory aspects of AI integration, paving the way for safer and more effective healthcare applications, ultimately benefiting patient care and outcomes.
Methodology: Alternatives: Incineration, Autoclave, Encapsulation, Distillation, Ozonation.
Evaluation Parameters: Waste residues, Process complexity, financial profit, Impact on quality of life.
Result: The results show that Autoclave received the highest ranking, whereas Ozonation received the lowest ranking.
Conclusion: Autoclave has the highest value for artificial intelligence and medicine according to the WASPAS approach.
