Healthcare increasingly relies on Artificial Intelligence (AI) to assist in various tasks, including decision-making, diagnosis, and treatment planning. However, integrating AI into healthcare presents challenges. These are primarily related to enhancing trust in its trustworthiness, which encompasses aspects such as transparency, fairness, privacy, safety, accountability, and effectiveness. Patients, doctors, stakeholders, and society need to have confidence in the ability of AI systems to deliver trustworthy healthcare. Explainable AI (XAI) is a critical tool that provides insights into AI decisions, making them more comprehensible (i.e., explainable/interpretable) and thus contributing to their trustworthiness. This topical collection explores the contribution of XAI in ensuring the trustworthiness of healthcare AI and enhancing the trust of all involved parties. In particular, the topical collection seeks to investigate the impact of trustworthiness on patient acceptance, clinician adoption, and system effectiveness. It also delves into recent advancements in making healthcare AI decisions trustworthy, especially in complex scenarios. Furthermore, it underscores the real-world applications of XAI in healthcare and addresses ethical considerations tied to diverse aspects such as transparency, fairness, and accountability.

We invite contributions to research into the theoretical underpinnings of XAI in healthcare and its applications. Specifically, we solicit original (interdisciplinary) research articles that present novel methods, share empirical studies, or present insightful case reports. We also welcome comprehensive reviews of the existing literature on XAI in healthcare, offering unique perspectives on the challenges, opportunities, and future trajectories. Furthermore, we are interested in practical implementations that showcase real-world, trustworthy AI-driven systems for healthcare delivery that highlight lessons learned.

We invite submissions related to the following topics (but not limited to):

- Theoretical foundations and practical applications of trustworthy healthcare AI: from design and development to deployment and integration.

- Transparency and responsibility of healthcare AI.

- Fairness and bias mitigation.

- Patient engagement.

- Clinical decision support.

- Patient safety.

- Privacy preservation.

- Clinical validation.

- Ethical, regulatory, and legal compliance.

This collection aims to bring together cutting-edge research and practical advancements at the intersection of artificial intelligence, big data analytics, finance, and management. As AI technologies and data-driven methodologies increasingly shape the future of financial services, corporate governance, and industrial decision-making, there is a growing need to explore their applications, implications, and innovations in real-world contexts.

The scope of this collection includes, but is not limited to, the following areas:

- AI models for financial forecasting, fraud detection, credit risk assessment, and regulatory compliance

- Machine learning techniques for portfolio optimization, stock price prediction, and trading strategies

- Data-driven approaches in corporate decision-making, performance evaluation, and strategic planning

- Intelligent systems for industrial optimization, logistics, and supply chain management

- Fintech innovations, digital assets, and algorithmic finance

- Ethical, regulatory, and societal considerations in deploying AI across financial and managerial domains

By highlighting both theoretical developments and real-world applications, this collection seeks to offer valuable insights to researchers, practitioners, and policymakers. Contributions that emphasize interdisciplinary approaches, practical relevance, and explainable AI are especially encouraged.

This Collection supports and amplifies research related to SDG 8 and SDG 9.

Keywords: AI in Finance, Accountability, Applied Machine Learning, Artificial Intelligence, Big Data