Cell：生成式AI真正理解生命，还要解决的十五大挑战
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Merged summary
TL;DR — A Cell perspective identifies 15 challenges generative AI must overcome to move from molecular modeling toward reliable cellular and clinical predictions. The supplied DeLiriuMAgents summary describes a separate clinical AI system and cannot be confidently merged into the same work.
- The 15 challenges cover molecular networks, synthetic biology, cell-state control, biomarkers, drug safety and efficacy, immune responses, and clinical-trial outcomes.
- Key barriers include scarce perturbation data, complex biological interactions, sequence-centric model architectures, limited clinical data sharing, and insufficient cohort diversity.
- Proposed directions include embedding biological priors—such as protein-interaction and gene-regulatory networks—into models and creating datasets tailored to individual challenges.
- Evaluation should shift from retrospective, largely solved benchmarks toward prospective experiments and blind CASP- or DREAM-style assessments.
- Progress requires sustained collaboration among computational scientists, experimentalists, clinicians, and ethicists.
Note: The second source focuses on DeLiriuMAgents for delirium prediction and appears unrelated to the titled Cell perspective, despite being grouped with it.
Sources (2)
Cell：生成式AI真正理解生命，还要解决的十五大挑战
TL;DR - A Cell perspective outlines 15 challenges that generative AI must overcome to progress from molecular successes such as protein modeling to reliable predictions of cellular behavior and clinical outcomes. It argues that scarce perturbation data, complex biological interactions, and sequence-centric architectures require biology-informed models and prospective evaluation.
- The challenges span molecular networks, synthetic biology, cell-state control, biomarkers, drug toxicity and efficacy, immune responses, and clinical-trial outcomes.
- The authors propose incorporating biological priors, such as protein-interaction and gene-regulatory networks, to constrain models and compensate for limited data.
- Current benchmarks often reward statistically significant gains on already-solved or retrospective tasks; the article calls for prospective experimental validation and CASP- or DREAM-style blind evaluations.
- Progress will require challenge-specific datasets, clinical data sharing, diverse cohorts, and sustained collaboration among computational scientists, experimentalists, clinicians, and ethicists.
Cell子刊：孔桂兰/马青变合作开发AI智能体系统，预测急诊重症患者的谵妄
TL;DR - Researchers developed DeLiriuMAgents, an LLM-driven multidisciplinary agent system for early delirium prediction in critically ill emergency patients. Its external validation across multiple cohorts suggests potential for transferable, interpretable clinical decision support.
- Combines machine-learning risk prediction, LLM-based emergency medicine, neurology, and psychiatry agents, plus RAG-sourced medical evidence.
- Uses MIMIC-IV for development and internal validation, with external validation on PKU multicenter and eICU-CRD cohorts.
- Accuracy/sensitivity/specificity were 0.749/0.762/0.747 on MIMIC-IV, 0.731/0.708/0.736 on PKU, and 0.670/0.708/0.665 on eICU-CRD.
- Chart review and clinician evaluation supported the generated risk reports’ interpretability and practical utility.