Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Pan, Lu-Chen; Wu, Xiao-Ru; Zhang, Hanqing; Lu, Ying; Zhou, Yao-Ling; Xue, L.; Liu, Shenglin; Yan, Qiaoyuan

doi:10.1016/j.apjon.2022.100127

Cited by 12 publications

(13 citation statements)

References 117 publications

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“…Artificial intelligence is now being used across the spectrum of cancer care from clinical trials to survivorship. 60 LLMs refer to a category of AI systems that are trained on up to trillions of text words on the internet, which allows them to predict the previous and next likely token (word) that follows in a sequence or sentence. LLM-based AI technologies such as ChatGPT can now respond to prompt queries (text or imaging) without being previously trained on the task or prompt, generating almost immediate text-based, human-level responses.…”

Section: The Interface Of Artificial Intelligence and Telemedicine In...mentioning

confidence: 99%

“…Artificial intelligence is now being used across the spectrum of cancer care from clinical trials to survivorship 60 . LLMs refer to a category of AI systems that are trained on up to trillions of text words on the internet, which allows them to predict the previous and next likely token (word) that follows in a sequence or sentence.…”

Section: The Interface Of Artificial Intelligence and Telemedicine In...mentioning

confidence: 99%

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Telemedicine and Cancer Clinical Research

von Itzstein,

Gwin,

Gupta

et al. 2024

Cancer J

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Telemedicine represents an established mode of patient care delivery that has and will continue to transform cancer clinical research. Through telemedicine, opportunities exist to improve patient care, enhance access to novel therapies, streamline data collection and monitoring, support communication, and increase trial efficiency. Potential challenges include disparities in technology access and literacy, physical examination performance, biospecimen collection, privacy and security concerns, coverage of services by insurance, and regulatory considerations. Coupled with artificial intelligence, telemedicine may offer ways to reach geographically dispersed candidates for narrowly focused cancer clinical trials, such as those targeting rare genomic subsets. Collaboration among clinical trial staff, clinicians, regulators, professional societies, patients, and their advocates is critical to optimize the benefits of telemedicine for clinical cancer research.

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Section: The Interface Of Artificial Intelligence and Telemedicine In...mentioning

confidence: 99%

Section: The Interface Of Artificial Intelligence and Telemedicine In...mentioning

confidence: 99%

Telemedicine and Cancer Clinical Research

von Itzstein,

Gwin,

Gupta

et al. 2024

Cancer J

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“…[27][28][29][30][31][32] LLMs may be most transformative in enabling patientcentered remote supportive care and symptom management and data collection, which are essential to improving cancer outcomes. [33][34][35][36] Some studies have demonstrated a quality of life and survival benefit of digital health solutions for symptom monitoring and management, although results vary across studies and specific outcomes. [35][36][37] Studies incorporating digital health solutions with AI have focused broadly on education, behavior change, and symptom management, particularly pain, anxiety, and depression.…”

Section: Potential Of Llmsmentioning

confidence: 99%

Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care

Bitterman,

Downing,

Maués

et al. 2024

JCO

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“…Market segmentation is usually referred as the process of classifying or grouping customers with different characteristics and behavior to implement more efficient marketing strategies and tactics (Kotler and Armstrong, 2006). Some of the most studied segmentation types in the literature are:…”

Section: Market Segmentationmentioning

confidence: 99%

“…Along with new challenges, innovative technologies have also emerged to facility the work in health centers and hospitals. Some good examples of these widely accepted new trends are: the so-called P4 medicine, where P4 stands for Predictive, Personalized, Preventive and Participatory (Ruiz and Velásquez, 2023), the advances in clinical medicine based on artificial intelligence (Pan et al, 2022), and the increasing use of machine learning algorithms to detect and to diagnose diseases using existing patient data (Kejriwal and Rajagopalan, 2023). Engineering and Operations Management, Sydney, Australia, December 20-21, 2022 According to Kotler (2001), organizations must identify the user segments to identify specific user needs.…”

Section: Introductionmentioning

confidence: 99%

Comparing Segmentations Based on Clustering Algorithms to Classify Patients of Intensive Care Units

Proceedings of the International Conference on Industrial Engineering and Operations Management

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It is common to classify patients that arrive at an intensive care unit (ICU) by means of a classification based on gender, age and health record. However, it is interesting and helpful to take advantage of additional data to propose alternative patient segmentation that might help allocate more efficiently the existing infrastructure, supplies, medical staff. In this investigation several ICU patients' segmentations were implemented and compared. Different from a supervised task like classification, where datasets have to be a priori labelled to train and to test prediction models, clustering algorithms requires no labelling. Instead, data are grouped according to their degree of similarity. The research was carried out following a 4-phase methodology: analysis, design, development, and validation. During the analysis, a large database with record of the medical care received by patients at the ICU of a public hospital located in the south of Chile was preprocessed and analyzed. During the design, several datasets were prepared to conduct experiments. At this point, the advantages and disadvantages of different clustering algorithms were analyzed and compared, selecting Simple K-Means Algorithm (SKMA) and Expectation-Maximization Clustering (EMC) to proceed with the investigation. Whereas SKMA creates clusters of equal variance, EMC assumes a Gaussian distribution of data. The phase of development was carried out using the data mining software WEKA 3.9.6. To complete the investigation, four datasets of five, ten, fifteen, and twenty thousand ICU records were used. Since no target class was defined, the clustering was the result of applying the selected algorithms: EMC and SKMA. For both cases, different number of clusters (k) were required to establish a comparison. Results revealed clear differences in the outputs generated by each clustering algorithm. For instance, with 5 clusters (k=5) EMC distributes data in the following proportions: 19%, 15%, 10%, 43%, and 28%. With SKMA, instead, the proportion were: 44%, 13%, 10%m 9%, and 24%. In conclusion, the investigation showed that popular clustering algorithms such as EMC and SKMA can be used for segmenting not only consumers but also ICU patients according to criteria that are not easy to visualize with classical tools and techniques. An adequate segmentation can provide valuable information to help estimate the requirement of medical staff, supplies and infrastructure, and also to define specific healthcare services.

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Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Cited by 12 publications

References 117 publications

Telemedicine and Cancer Clinical Research

Telemedicine and Cancer Clinical Research

Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care

Comparing Segmentations Based on Clustering Algorithms to Classify Patients of Intensive Care Units

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