This article explores the importance of explainability in artificial intelligence (AI) within healthcare, emphasizing its multidisciplinary implications. Explainability refers to the ability of AI systems to provide understandable reasoning for their decisions, which is crucial for clinical adoption. The paper examines explainability from technological, legal, medical, and patient perspectives, highlighting the ethical considerations involved. Technologically, explainability is essential for understanding how AI models operate and for ensuring their reliability. Legal considerations include informed consent, certification of medical devices, and liability. From a medical perspective, explainability helps in distinguishing AI-based clinical decision support systems (CDSS) from traditional diagnostic tools and ensures that clinicians can validate AI outputs. Patients benefit from explainability as it supports shared decision-making and ensures they understand the rationale behind AI recommendations. Ethically, explainability aligns with the principles of autonomy, beneficence, non-maleficence, and justice. It ensures that patients are informed and can make autonomous decisions, that AI systems promote patient well-being, that they do not cause harm, and that they provide equitable access to healthcare. The paper argues that without explainability, AI systems may fail to meet ethical standards, leading to potential harm and loss of trust. The study concludes that explainability is essential for the responsible development and application of AI in healthcare. It ensures that AI systems are transparent, trustworthy, and aligned with ethical and professional standards. Developers, healthcare professionals, and policymakers must collaborate to address the challenges and limitations of opaque algorithms, ensuring that AI is used ethically and effectively in clinical practice.This article explores the importance of explainability in artificial intelligence (AI) within healthcare, emphasizing its multidisciplinary implications. Explainability refers to the ability of AI systems to provide understandable reasoning for their decisions, which is crucial for clinical adoption. The paper examines explainability from technological, legal, medical, and patient perspectives, highlighting the ethical considerations involved. Technologically, explainability is essential for understanding how AI models operate and for ensuring their reliability. Legal considerations include informed consent, certification of medical devices, and liability. From a medical perspective, explainability helps in distinguishing AI-based clinical decision support systems (CDSS) from traditional diagnostic tools and ensures that clinicians can validate AI outputs. Patients benefit from explainability as it supports shared decision-making and ensures they understand the rationale behind AI recommendations. Ethically, explainability aligns with the principles of autonomy, beneficence, non-maleficence, and justice. It ensures that patients are informed and can make autonomous decisions, that AI systems promote patient well-being, that they do not cause harm, and that they provide equitable access to healthcare. The paper argues that without explainability, AI systems may fail to meet ethical standards, leading to potential harm and loss of trust. The study concludes that explainability is essential for the responsible development and application of AI in healthcare. It ensures that AI systems are transparent, trustworthy, and aligned with ethical and professional standards. Developers, healthcare professionals, and policymakers must collaborate to address the challenges and limitations of opaque algorithms, ensuring that AI is used ethically and effectively in clinical practice.