Translational gaps in immuno-AI: From algorithmic accuracy to clinical trust

Area of Science:

• Oncology
• Artificial Intelligence
• Translational Medicine

Background:

• Artificial intelligence (AI) demonstrates significant potential in predicting patient responses to immune checkpoint inhibitors (ICIs) in cancer treatment.
• Despite high statistical accuracy, the clinical adoption of these AI models, known as Immuno-AI, remains limited, creating a 'translational gap'.

Purpose of the Study:

• To critically review the barriers hindering the clinical translation of Immuno-AI systems from research prototypes to clinical decision-support tools.
• To analyze methodological, regulatory, ethical, and infrastructural factors impeding the implementation of AI in immuno-oncology.
• To propose strategies for developing clinically trustworthy AI in immuno-oncology.

Main Methods:

• A structured literature search was performed across major databases (PubMed, Embase, Scopus, Web of Science) for studies published between 2018 and 2025.
• The search focused on AI and machine learning models predicting ICI response or toxicity in human cohorts.
• Narrative synthesis was employed to identify and analyze translational bottlenecks.

Main Results:

• Three primary factors contribute to the translational gap: insufficient external/prospective validation leading to performance overestimation, limited interpretability and lack of explainable AI (XAI) frameworks, and immature regulatory/infrastructural frameworks for adaptive AI.
• These limitations erode clinician confidence and impede regulatory approval.
• Current Immuno-AI systems often prioritize algorithmic optimization over clinical utility and accountability.

Conclusions:

• Bridging the translational gap in Immuno-AI necessitates a shift towards system-level accountability, emphasizing robust validation, interpretability, and ethical governance.
• Clinically trustworthy AI requires validation across diverse institutions and transparent frameworks.
• Collaborative efforts between researchers, clinicians, and regulators are crucial for Immuno-AI to achieve clinical credibility and social legitimacy.