Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

Abstract

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning– and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision–assisted image analysis in particular having several U.S. Food and Drug Administration–approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI’s “black box” mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model’s complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person’s health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

“…In particular, one AI model for MSI detection had lower performance in a cohort involving a high number of Ashkenazi Jews due to their elevated proportion of the BRAF mutation ( 69 ). To this end, there may also be overrepresentation of certain groups and the presence of inter-rater variability in the data-labelling process ( 111 , 112 ). For this reason, algorithms must be externally validated in which outcomes are reproduced in different contexts to ensure the rigor of the models.…”

“…Other concerns involve ethical responsibility and accountability in the presence of AI errors. There is also a lack of structure and standardization regarding the storage and collection of data ( 111 , 112 ).…”

Translating Data Science Results into Precision Oncology Decisions: A Mini Review

“…The evaluation of the efficacy deals with the determination of numerical estimators able to quantify the performance of the new method as precision, accuracy, robustness, etc. Despite the importance of the topic, there are no worldwide accepted regulations that define harmonized standards to evaluate the performance of AI methods [ 28 , 29 , 30 ]. A first group of guidance rules has been defined in the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD-AI [ 31 ]).…”

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

“…For the analysis of distributed data, advanced-federated learning models can be deployed. Whole-blood, multi-cancer detection using deep sequencing, virtual biopsies, and NLP to infer health trajectories from medical records, and advanced clinical-decision support systems that incorporate genomics and clinomics, are some of the emerging clinically useful techniques [18]. Oncology heavily relies on evidence-based, medicine-scoring systems for cancer-risk assessment, disease diagnosis, prognostic staging, treatment, and surveillance monitoring.…”