Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching “chronic myeloid leukemia” and “artificial intelligence”. The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the ‘human’ factor, which remains key in this domain.
人工智能正逐渐成为一门能够为医学领域带来显著附加价值的学科，尤其在放射组学、影像分析、大数据集分析以及生成虚拟患者队列方面表现突出。然而，在应对慢性髓系白血病这一领域，尽管酪氨酸激酶抑制剂的引入已大幅提升患者预期寿命，使该疾病被视为易于管理的恶性肿瘤，但人工智能的应用仍处于起步阶段。值得注意的是，初期试验的结果无论在性能表现还是对不同应用场景的适应性方面，都展现出引人入胜且鼓舞人心的潜力。事实上，通过整合生化、生物分子、影像及临床数据来提升诊断与预后评估水平，对于推动个体化医疗模式的实施或优化诊疗流程与服务至关重要。本综述系统阐述了人工智能在慢性髓系白血病领域应用的最新进展，详细介绍了相关技术方法与目标，并突破机器学习的局限，以更广阔的人工智能领域为视角展开论述。本次范围综述涵盖2003年至2023年间PubMed数据库中以"慢性髓系白血病"和"人工智能"为关键词检索到的文献，该时间跨度客观反映了该领域的实际文献产出情况，未作人为限制。同时，我们借此机会探讨了人工智能必须应对的主要挑战与关键问题，特别强调了"人为因素"在这一领域持续发挥的核心作用。
肿瘤(癌症)患者之家