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

Dankwa‐Mullan, Irene; Weeraratne, Dilhan

doi:10.1158/2159-8290.cd-22-0373

Cited by 32 publications

(11 citation statements)

References 13 publications

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“…AI has many advantages compared with traditional strategies for addressing health disparities, notably in its ability to uncover unexpected correlations and relationships that have remained unidentified in human-driven analyses, offering new insights. By examining variables like genetic markers, environmental exposures, social determinants, and zip codes, AI can uncover novel connections that challenge current paradigms, revealing new areas for research and interventions ( 14 ).…”

Section: Ai As a Tool To Address Health Disparitiesmentioning

confidence: 99%

“…Fifth and finally, there are concerns that becoming overly reliant on AI may undermine the clinician-patient relationship ( 14 ). Though algorithms cannot and should not replace human interactions in healthcare, incorporating AI as a decision support tool rather than a replacement for humans can harness and balance both the physician's expertise and machine's insights.…”

Section: Challenges To Aimentioning

confidence: 99%

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Accelerating health disparities research with artificial intelligence

Green,

Murphy,

Robinson

2024

Front. Digit. Health

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Section: Ai As a Tool To Address Health Disparitiesmentioning

confidence: 99%

Section: Challenges To Aimentioning

confidence: 99%

Accelerating health disparities research with artificial intelligence

Green,

Murphy,

Robinson

2024

Front. Digit. Health

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“…A potentially significant, yet subtle, consequence of improper data collection might be an algorithm that performs poorly for certain subgroups or subpopulations with the targeted disease or condition as a result of under‐representation of those subgroups in the training set 30,31 . In radiology applications, it is important to be vigilant so that training/validation dataset selection incorporates safeguards to minimize underlying distortions for under‐represented and/or vulnerable populations and so that already‐existing health‐care inequities are not perpetuated or exacerbated 27,32–34 …”

Section: Datamentioning

confidence: 99%

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging

et al. 2023

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Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer‐aided diagnosis (CAD) development and applications using both “traditional” machine learning methods and newer DL‐based methods. We use the term CAD‐AI to refer to this expanded clinical decision support environment that uses traditional and DL‐based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer‐aided, or AI‐assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer‐Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer‐aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD‐AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD‐AI systems for clinical decision support.

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“…For such studies, adequately sized datasets corrected for such biases are needed to analyze the effects of covariates accurately. Several recommendations for addressing biases have been presented in [ 74 ].…”

Section: Limitations Challenges and Recommendationsmentioning

confidence: 99%

Unleashing the potential of AI for pathology: challenges and recommendations

Asif

Rajpoot

Graham

et al. 2023

The Journal of Pathology

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Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer‐reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre‐clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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

Cited by 32 publications

References 13 publications

Accelerating health disparities research with artificial intelligence

Accelerating health disparities research with artificial intelligence

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging

Unleashing the potential of AI for pathology: challenges and recommendations

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