What Are the Interoperability Requirements for Clinical AI?

Author: Rasit Dinc

Introduction

Artificial intelligence (AI) is poised to revolutionize healthcare, offering the potential to improve diagnostics, personalize treatments, and streamline clinical workflows. However, the effective implementation of clinical AI hinges on a critical factor: interoperability. In the context of healthcare, interoperability refers to the ability of different information systems, devices, and applications to access, exchange, integrate, and cooperatively use data in a coordinated manner. Without seamless interoperability, the transformative power of AI in clinical settings will remain limited, siloed within individual systems and institutions. This article will explore the key interoperability requirements for clinical AI, the challenges to achieving them, and the emerging solutions that promise a more integrated and intelligent healthcare future.

The Core Interoperability Requirements for Clinical AI

To fully realize the potential of AI in clinical practice, a multi-faceted approach to interoperability is required, encompassing data, semantic, technical, and organizational dimensions. These requirements are not mutually exclusive but rather interconnected and interdependent, forming the foundation for a robust and reliable clinical AI ecosystem.

Data and Semantic Interoperability: At its core, clinical AI relies on high-quality, comprehensive, and diverse data. Data interoperability ensures that AI systems can access and exchange data from various sources, including electronic health records (EHRs), medical imaging archives, laboratory information systems, and wearable devices. However, simply exchanging data is not enough. Semantic interoperability is crucial to ensure that the meaning of the data is preserved and understood across different systems. This requires the use of standardized terminologies, ontologies, and data models, such as SNOMED CT, LOINC, and FHIR [1]. Without semantic interoperability, AI algorithms may misinterpret data, leading to inaccurate predictions and potentially harmful clinical decisions.

Technical Interoperability: Technical interoperability refers to the ability of different systems and applications to communicate and exchange data seamlessly. This requires the use of standardized communication protocols, application programming interfaces (APIs), and data formats. The adoption of modern, web-based standards like FHIR has been a significant step forward in achieving technical interoperability in healthcare [2]. FHIR provides a flexible and extensible framework for exchanging healthcare information, enabling developers to build innovative applications that can be easily integrated with existing EHR systems.

Organizational and Governance Interoperability: Beyond the technical aspects, achieving interoperability for clinical AI also requires a commitment from healthcare organizations to collaborate and share data. This involves establishing clear governance frameworks, data sharing agreements, and ethical guidelines for the use of AI in clinical practice. Organizational interoperability also requires a cultural shift towards greater transparency and a willingness to embrace new technologies and workflows. As noted by Abdelwanis et al. (2026), the lack of trust in AI systems among healthcare professionals can be a significant barrier to adoption [3]. Therefore, building trust through transparent and explainable AI is a critical component of organizational interoperability.

Challenges in Achieving Interoperability

Despite the clear need for interoperability in clinical AI, several significant challenges remain. These challenges span technical, organizational, and regulatory domains, and addressing them will require a concerted effort from all stakeholders in the healthcare ecosystem.

Legacy Systems and Data Silos: Many healthcare organizations still rely on legacy IT systems that were not designed for interoperability. These systems often use proprietary data formats and communication protocols, making it difficult to exchange data with other systems. This results in data silos, where valuable clinical information is trapped within individual departments or institutions, inaccessible to AI applications that could derive valuable insights from it [4].

Lack of Standardization: While progress has been made in developing healthcare data standards, their adoption is far from universal. Different organizations may use different standards, or even different versions of the same standard, leading to inconsistencies and data quality issues. The lack of standardized data models and terminologies makes it challenging to aggregate and analyze data from multiple sources, which is essential for training and validating robust AI models [5].

Data Privacy and Security Concerns: The use of AI in healthcare raises significant data privacy and security concerns. Clinical data is highly sensitive, and there is a risk that it could be misused or accessed by unauthorized individuals. Healthcare organizations must ensure that they have robust security measures in place to protect patient data, and that they comply with all relevant privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. The need for secure data sharing mechanisms is a critical prerequisite for building trust and facilitating the adoption of clinical AI [6].

Organizational and Cultural Barriers: As mentioned earlier, organizational and cultural barriers can be just as challenging as technical ones. A lack of trust in AI, resistance to change, and a lack of incentives for data sharing can all hinder the adoption of interoperable AI solutions. Overcoming these barriers will require strong leadership, effective communication, and a clear demonstration of the value that AI can bring to clinical practice.

Emerging Solutions and the Path Forward

Despite the challenges, there is a growing momentum towards achieving greater interoperability for clinical AI. Several emerging solutions and strategies are paving the way for a more integrated and intelligent healthcare ecosystem.

The Rise of FHIR and Open APIs: The widespread adoption of the FHIR standard is a game-changer for healthcare interoperability. FHIR's modern, web-based approach makes it easier for developers to create applications that can seamlessly exchange data with EHRs and other clinical systems. The use of open APIs, based on FHIR and other standards, is fostering a more open and collaborative ecosystem, where data can flow more freely between different systems and applications [2].

Cloud Computing and Scalable Infrastructure: Cloud computing platforms, such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure, provide the scalable and flexible infrastructure needed to support large-scale clinical AI applications. These platforms offer a range of services for data storage, processing, and analysis, making it easier for healthcare organizations to deploy and manage AI models. Cloud-based solutions can also help to break down data silos by providing a centralized repository for clinical data from multiple sources [7].

Federated Learning and Privacy-Preserving AI: Federated learning is a machine learning technique that allows AI models to be trained on decentralized data, without the need to share the underlying data itself. This approach has significant potential for healthcare, as it allows organizations to collaborate on training AI models without compromising patient privacy. By keeping sensitive data within the local institution, federated learning can help to overcome some of the privacy and security barriers to data sharing [8].

Regulatory Initiatives and Incentives: Governments and regulatory bodies are also playing a crucial role in promoting interoperability. Initiatives such as the 21st Century Cures Act in the United States are mandating the use of standardized APIs and promoting greater data sharing among healthcare providers. These regulatory efforts are creating a stronger incentive for healthcare organizations to invest in interoperable IT systems and to embrace a more collaborative approach to data sharing [9].

Conclusion

Interoperability is not merely a technical requirement but a strategic imperative for the successful integration of AI into clinical practice. Achieving seamless data exchange, semantic understanding, and technical compatibility across the healthcare ecosystem is essential to unlock the full potential of AI to improve patient outcomes, enhance clinical decision-making, and drive down costs. While significant challenges remain, the convergence of new standards like FHIR, the power of cloud computing, and innovative approaches like federated learning, coupled with a supportive regulatory environment, offers a clear path forward. By embracing a collaborative and standards-based approach to interoperability, the healthcare industry can build a future where data flows freely and securely, empowering AI to transform the delivery of care for generations to come.