Explainable Artificial Intelligence (AI) in Medical Imaging is an emerging technology aimed at revolutionizing healthcare diagnostics and treatments by providing sophisticated imaging analytic capabilities that are not only utilizable, but thoroughly interpretable by clinical experts.

Explainable AI in Medical Imaging exhibits crucial features:

• High Efficiency: AI algorithms swiftly analyse complex medical imaging data, enhancing diagnostic precision and potency.
• Advanced Interpretability: The algorithm is not a 'black-box'; it provides clear reasoning behind its output, enabling healthcare professionals to understand and trust the derived insights.
• Improved Patient Care: With accurate, quick, and understandable results, practitioners can develop advanced care plans, thereby improving patient outcomes.
• Collaborative Learning: This technology learns from new datasets, continually enhancing its predictive accuracy and clinical value.
• Transformative Potential: It can reform existing clinical paradigms by facilitating personalised care and precision medicine.

Many sectors, particularly healthcare providers, utilize Explainable AI in Medical Imaging due to its interpretability, efficiency, and potential to improve patient care.

For successful implementation, one needs a meticulously planned approach. You should understand the specific needs of your setup and evaluate if the utility of the AI system aligns with these needs. It’s also necessary to assess the cost-benefit ratio, the vendor’s capabilities, and their ability to provide ongoing support.

Your plan should also address the training needs of your staff to ensure they can effectively use and interpret the insights provided by the AI system. Also equally important is a focused approach towards integrating the AI system with your existing infrastructure and managing data security efficiently.

Explainable AI in Medical Imaging advances healthcare in multiple ways, including:

• Cost and Time Efficient: Unlike conventional human-led methodologies, AI can swiftly and precisely interpret imaging data, thereby saving time, reducing costs, and enhancing patient outcomes.
• Greater Accuracy: By leveraging powerfully intuitive algorithms, Explainable AI ensures superior diagnostic accuracy, reducing human error.
• Interpretability: It provides clear, comprehensive explanations of the decisions made by the algorithm, fostering trust and understanding among healthcare practitioners.
• Consistent Learning: As new data comes in, our system continues to evolve and improve, leading to enhanced accuracy and efficiency in the diagnostic process.
• Patient-centric Technology: Explainable AI can support personalised patient care, enabling tailor-made treatment plans based on individual diagnostics.

However, with every technological advance comes potential drawbacks; here are some associated with Explainable AI in Medical Imaging:

• Data Privacy Issues: AI systems rely on large datasets, giving rise to potential privacy issues. Ensuring robust and secure data protection measures are crucial.
• High Initial Costs: Investment in AI technology can be substantial, including costs for system integration, training, and ongoing maintenance.
• Regulatory Challenges: As a new frontier in healthcare, AI integration faces regulatory scrutiny and must comply with existing patient safety and data protection legislations.
• Dependence on Data Quality: The accuracy and interpretability of the AI rely heavily on the quality and diversity of the input data; any bias or flaw in the data can affect the algorithm’s performance.
• Reliance on Vendor Support: Continued dependability on the vendor for upgrades, support, and system enhancements can create an unwelcome dependency.

Overall, the use of Explainable AI in Medical Imaging has the potential to bring about a significant transformation in healthcare, particularly in diagnostics and treatment planning. However, careful implementation and management of these advanced tools are key to leveraging their full potential. The use of such technology should always align with the ultimate goal of any healthcare setup - improved patient outcomes.