Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Fenech, Matthew

doi:10.1016/j.clon.2019.09.053

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…Finally, the issue of fairness arises with algorithm bias when data used to train and test chatbots do not accurately reflect the people they represent [ 101 ]. As the AI field lacks diversity, bias at the level of the algorithm and modeling choices may be overlooked by developers [ 102 ]. In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [ 103 ].…”

Section: Discussionmentioning

confidence: 99%

“…The US Food and Drug Administration has recognized the distinctiveness of chatbots compared with traditional medical devices by defining the software within the medical device category and has outlined its approach through the Digital Health Innovation Action Plan [ 108 ]. With the growing number of AI algorithms approved by the Food and Drug Administration, they opened public consultations for setting performance targets, monitoring performance, and reviewing when performance strays from preset parameters [ 102 ]. The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [ 109 ].…”

Section: Discussionmentioning

confidence: 99%

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Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Xu¹,

Sanders²,

Li³

et al. 2021

JMIR Cancer

228

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Background Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Xu¹,

Sanders²,

Li³

et al. 2021

JMIR Cancer

228

View full text Add to dashboard Cite

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“…For more accurate patient-level predictions and for modeling disease prognosis and risk prediction, data mining techniques and adaptive ML algorithms have consistently outperformed traditional statistical approaches [7] . ML-based techniques have the advantage of being able to automate the process of hypothesis formulation and evaluation, while assigning parameter weights to predictors based on correlates with the outcome prediction [6] , [8] . Despite this, the enormous promise of AI in cancer research should be carefully addressed alongside answers to the challenges of transparency and reproducibility [9] , [10] , [11] .…”

Section: Introductionmentioning

confidence: 99%

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

Κούρου

Exarchos

Papaloukas

et al. 2021

Computational and Structural Biotechnology Journal

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Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

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A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects

Κούρου

Manikis

Poikonen‐Saksela

et al. 2021

Computers in Biology and Medicine

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Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Cited by 4 publications

References 24 publications

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects

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