Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?

Abstract

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.

Keywords: artificial intelligence; chronic myeloid leukemia; machine learning; minimal residual disease; risk assessment.

Figure 1

Graphical report about the number of publications concerning Chronic Myeloid Leukemia and Artificial Intelligence reported in PubMed.

Figure 2

Timeline of clinical trial applying AI in the setting of CML. The trials are divided based on the AI strategies reported. (a) the timeline of publications by year and clustered by technique. The numbers refer to the IDs in the Table 2 (b) An upset plot showing how often the techniques were used. Mostly they have been used alone (the first 4 vertical bars: 9 + 3 + 2 + 2 = 16 times) and only 7 times more than one technique has been used in a paper. Only one paper tested all the four techniques (the last vertical bar, on the right). The horizontal bars show the usage of any cluster.