Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, MN, United States.
Scientific Affairs, Hansa Biopharma AB, Lund, Sweden.
Front Immunol. 2021 Jun 11;12:694222. doi: 10.3389/fimmu.2021.694222. eCollection 2021.
Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decision making by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).
系统免疫学的进步,如新型生物标志物,为高度个性化的免疫抑制方案提供了潜力,从而改善患者的预后。未来,将所有这些信息与其他患者病史数据相结合,可能不得不依赖人工智能 (AI)。AI 代理可以通过发现模式并对特定患者进行预测来帮助增强移植决策,这些预测在文献中未涵盖或人类无法通过整合大量数据(例如在众多生物标志物上的趋势)来预测。与其他临床决策支持系统类似,AI 可能有助于克服人为偏见或判断错误。然而,迄今为止,AI 在移植中并未得到广泛应用。在本次快速审查中,我们调查了最近与移植相关的 AI 应用研究中采用的方法,并确定了与实施这些工具相关的问题。我们确定了阻碍 AI 在移植中应用的三个关键挑战(偏差/准确性、临床决策过程/AI 可解释性、AI 可接受性标准)。我们还确定了在短期内可以采取的步骤,以帮助推进 AI 在移植中的实际应用(在每个中心成立移植 AI 团队、建立临床和伦理可接受性标准,并将 AI 纳入共享决策模型)。