High-dimensional role of AI and machine learning in cancer research

Capobianco, Enrico

doi:10.1038/s41416-021-01689-z

Cited by 33 publications

(19 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With such a complex dataset, researchers have employed artificial intelligence tools to identify risk factors for cancer incidence and to improve risk prediction models for cancer onset and survival [74,75]. For example, machine-learning algorithms have been used to predict overall survival in breast cancer patients from whole-exome sequencing data in UK Biobank [76].…”

Section: Uk Biobank and Cancer Researchmentioning

confidence: 99%

UK Biobank: a globally important resource for cancer research

et al. 2022

View full text Add to dashboard Cite

UK Biobank is a large-scale prospective study with deep phenotyping and genomic data. Its open-access policy allows researchers worldwide, from academia or industry, to perform health research in the public interest. Between 2006 and 2010, the study recruited 502,000 adults aged 40–69 years from the general population of the United Kingdom. At enrolment, participants provided information on a wide range of factors, physical measurements were taken, and biological samples (blood, urine and saliva) were collected for long-term storage. Participants have now been followed up for over a decade with more than 52,000 incident cancer cases recorded. The study continues to be enhanced with repeat assessments, web-based questionnaires, multi-modal imaging, and conversion of the stored biological samples to genomic and other ‘–omic’ data. The study has already demonstrated its value in enabling research into the determinants of cancer, and future planned enhancements will make the resource even more valuable to cancer researchers. Over 26,000 researchers worldwide are currently using the data, performing a wide range of cancer research. UK Biobank is uniquely placed to transform our understanding of the causes of cancer development and progression, and drive improvements in cancer treatment and prevention over the coming decades.

show abstract

Section: Uk Biobank and Cancer Researchmentioning

confidence: 99%

UK Biobank: a globally important resource for cancer research

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A plethora of supervised and unsupervised ML and DL models continue to be developed and explored for improving the accuracy of a pancreatic cancer diagnosis at the early stage which could be invaluable in enhancing the survival of the affected individual [ 20 ]. The complexity of the algorithms will reflect the type of functions they can perform ranging from feature extraction, simple clustering or segregation of data, classification of data, prediction, forecasting, and decision-making [ 21 ]. Algorithms such as Naive–Bayes, support vector machine, linear regression analysis, ensemble methods, decision tree, K-mode, hidden Markov model, hierarchical, Gaussian mixture, and neural networks have all been explored with different imaging data sets for distinguishing cancerous tissue from non-cancerous tissues [ 22 ].…”

Section: Artificial Intelligence For Diagnostic Applicationsmentioning

confidence: 99%

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Hameed

Krishnan

2022

Cancers

View full text Add to dashboard Cite

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians’ treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

show abstract

“…There exists a wide body of literature on the barriers to and facilitators of implementing AI in healthcare [ 3 , 4 , 5 ]. However, much of what we know about these barriers and facilitators comes from anecdotal evidence [ 6 ], narrative commentaries [ 7 ] and reviews [ 8 , 9 , 10 , 11 ], mostly without any empirical support or sound theoretical basis. As a result, the determinants of AI implementation success in healthcare are still poorly understood [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Chomutare

Tejedor

Svenning

et al. 2022

IJERPH

View full text Add to dashboard Cite

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

show abstract

High-dimensional role of AI and machine learning in cancer research

Cited by 33 publications

References 106 publications

UK Biobank: a globally important resource for cancer research

UK Biobank: a globally important resource for cancer research

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Contact Info

Product

Resources

About