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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...

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Related Experiment Videos

Translational Gaps in Immuno-AI: From algorithmic accuracy to clinical trust.

Emmanuel O Oisakede1, Raphael Igbarumah Ayo Daniel2, Olabanke Florence Olawuyi3

  • 1Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Department of Health Research, University of Leeds, Leeds, United Kingdom.

Human Immunology
|June 10, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise in predicting cancer patient responses to immune checkpoint inhibitors (ICIs). However, clinical use lags due to validation, interpretability, and regulatory hurdles, creating a significant translational gap for Immuno-AI systems.

Keywords:
Artificial intelligenceClinical translationImmune checkpoint inhibitorsImmunotherapyInterpretabilityValidation

Related Experiment Videos

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.