Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity.

Artificial intelligence (AI) and machine learning (ML) technologies have not only tremendous potential to augment clinical decision-making and enhance quality care and precision medicine efforts, but also the potential to worsen existing health disparities without a thoughtful, transparent, and inclusive approach that includes addressing bias in their design and implementation along the cancer discovery and care continuum. We discuss applications of AI/ML tools in cancer and provide recommendations for addressing and mitigating potential bias with AI and ML technologies while promoting cancer health equity.

Dankwa-Mullan I ，Weeraratne D 《-》

Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care.

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.

Corti C ，Cobanaj M ，Dee EC ，Criscitiello C ，Tolaney SM ，Celi LA ，Curigliano G ... -  《-》

Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective.

Gao Y ，Sharma T ，Cui Y 《-》

Big Data, Machine Learning, and Artificial Intelligence to Advance Cancer Care: Opportunities and Challenges.

The rapid advances in artificial intelligence (AI), big data, and machine learning (ML) technologies hold promise for personalized, equitable cancer care and improved health outcomes within the context of cancer and beyond. Furthermore, integrating these technologies into cancer research has been effective in addressing many of the challenges for cancer control and cure. This can be achieved through the insights generated from massive amounts of data, in ways that can help inform decisions, interventions, and precision cancer care. AI, big data, and ML technologies offer, either in isolation or in combination, unconventional pathways that facilitate the better understanding and management of cancer and its impact on the person. The value of AI, big data, and ML technologies has been acknowledged and integrated within the Cancer Moonshot program in the U.S. and the EU Beating Cancer Plan in Europe. Relevant studies on the topic have formed the basis for this article. In a shifting health care environment where cancer care is becoming more complex and demanding, big data and AI technologies can act as a vehicle to facilitating the care continuum. An increasing body of literature demonstrates their impactful contributions in areas such as treatment and diagnosis. These technologies, however, create additional requirements from health care professionals in terms of capacity and preparedness to integrate them effectively and efficiently in clinical practice. Therefore, there is an increasing need for investment and training in oncology to combat and overcome some of the challenges posed by cancer control. AI, big data, and ML are increasingly integrated in various aspects of health care. As a result, health care professionals, including nurses, will need to adjust in an ever-changing practice environment where these technologies have potential applications in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.

Charalambous A ，Dodlek N 《-》

Artificial Intelligence for Precision Oncology.

Precision oncology is an innovative approach to cancer care in which diagnosis, prognosis, and treatment are informed by the individual patient's genetic and molecular profile. The rapid development of novel high-throughput omics technologies in recent years has led to the generation of massive amount of complex patient data, which in turn has prompted the development of novel computational infrastructures, platforms, and tools to store, retrieve, and analyze this data efficiently. Artificial intelligence (AI), and in particular its subfield of machine learning, is ideal for deciphering patterns in large datasets and offers unique opportunities for advancing precision oncology. In this chapter, we provide an overview of the various public data resources and applications of AI in precision oncology and cancer research, from subtype identification to drug prioritization, using multi-omics datasets. We also discuss the impact of AI-powered medical image analysis in oncology and present the first diagnostic FDA-approved AI-powered tools.

Bhalla S ，Laganà A 《Advances in Experimental Medicine and Biology》