The impact of artificial intelligence on health equity in oncology: A scoping review (Preprint)
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Ross Upshur | B. Gyawali | J. Burkell | B. Chin-Yee | B. Sadikovic | A. Lazo-Langner | Alla E. Iansavichene | Paul Istasy | Wen Shen Lee
[1] B. Gyawali,et al. Cancer Groundshot: Building a Robust Cancer Control Platform in Addition To Launching the Cancer Moonshot. , 2022, American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting.
[2] L. Erdman,et al. A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning , 2022, The American journal of bioethics : AJOB.
[3] A. Scarpa,et al. Artificial intelligence in oncology: current applications and future perspectives , 2021, British Journal of Cancer.
[4] Athina Tzovara,et al. Addressing bias in big data and AI for health care: A call for open science , 2021, Patterns.
[5] Joseph Donia,et al. The Sociotechnical Ethics of Digital Health: A Critique and Extension of Approaches From Bioethics , 2021, Frontiers in Digital Health.
[6] Kadija Ferryman. The Dangers of Data Colonialism in Precision Public Health , 2021, Global Policy.
[7] Jakob Nikolas Kather,et al. The impact of site-specific digital histology signatures on deep learning model accuracy and bias , 2021, Nature Communications.
[8] James Shaw,et al. Co-design and ethical artificial intelligence for health: An agenda for critical research and practice , 2021, Big Data & Society.
[9] Michelle S. Lee,et al. Towards gender equity in artificial intelligence and machine learning applications in dermatology , 2021, J. Am. Medical Informatics Assoc..
[10] Bofan Song,et al. Mobile-based oral cancer classification for point-of-care screening , 2021, Journal of biomedical optics.
[11] V. Shankaran,et al. AI2 The Impact of Including Race and Ethnicity in Risk Prediction Models on Racial Bias , 2021, Value in Health.
[12] M. Rutter,et al. Power of big data to improve patient care in gastroenterology , 2021, Frontline Gastroenterology.
[13] H. Aerts,et al. Artificial intelligence for clinical oncology. , 2021, Cancer cell.
[14] M. Mamas,et al. Machine Learning-Augmented Propensity Score Analysis of Percutaneous Coronary Intervention in Over 30 Million Cancer and Non-cancer Patients , 2021, Frontiers in Cardiovascular Medicine.
[15] R. Richards-Kortum,et al. Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: The CLARA study , 2021, International journal of cancer.
[16] Hyun Cheol Oh,et al. Prediction of the risk of developing hepatocellular carcinoma in health screening examinees: a Korean cohort study , 2021, BMC cancer.
[17] Z. Jie,et al. A meta-analysis of Watson for Oncology in clinical application , 2021, Scientific Reports.
[18] J. Lipoff,et al. Equity in skin typing: why it is time to replace the Fitzpatrick scale , 2021, The British journal of dermatology.
[19] N. Linder,et al. Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting , 2021, JAMA network open.
[20] H. Krumholz,et al. Abstract PO-074: The impact of phenotypic bias in the generalizability of deep learning models in non-small cell lung cancer , 2021 .
[21] C. Lehman. Abstract IA-21: AI in an imaging center: Challenges and opportunities , 2021 .
[22] Stefanos Boukovalas,et al. Development of Machine Learning Algorithms for the Prediction of Financial Toxicity in Localized Breast Cancer Following Surgical Treatment , 2021, JCO clinical cancer informatics.
[23] K. Meiburger,et al. The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks , 2021, Diagnostics.
[24] M. Danner,et al. Ten-Year Single Institutional Analysis of Geographic and Demographic Characteristics of Patients Treated With Stereotactic Body Radiation Therapy for Localized Prostate Cancer , 2021, Frontiers in Oncology.
[25] R. Barzilay,et al. Abstract SP080: Hidden clues in the mammogram: How AI can improve early breast cancer detection , 2021 .
[26] Anusha Bompelli,et al. Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review , 2021, Health Data Science.
[27] P. Babyn,et al. Development and Cost Analysis of a Lung Nodule Management Strategy Combining Artificial Intelligence and Lung Reporting and Data Systems for Baseline Lung Cancer Screening. , 2021, Journal of the American College of Radiology : JACR.
[28] P. Pandharipande,et al. Rethinking the Approach to Artificial Intelligence for Medical Image Analysis: The Case for Precision Diagnosis. , 2021, Journal of the American College of Radiology : JACR.
[29] A. Mecocci,et al. A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi. , 2020, Journal of dermatological science.
[30] Hongyi Ren,et al. Deep Learning Prediction of Cancer Prevalence from Satellite Imagery , 2020, Cancers.
[31] P. Chandrashekar,et al. Implementing a targeted approach to social determinants of health interventions. , 2020, The American journal of managed care.
[32] Bradley J. Nartowt,et al. Population-Based Screening for Endometrial Cancer: Human vs. Machine Intelligence , 2020, Frontiers in Artificial Intelligence.
[33] R. Timmerman,et al. Covering Gaps in Radiation Oncology Through Artificial Intelligence in Low-Resource Countries: A Survey-Based Analysis , 2020 .
[34] D. Lu,et al. Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study , 2020, JMIR medical informatics.
[35] A. Vogel,et al. Decision making biases in the allied health professions: A systematic scoping review , 2020, PloS one.
[36] D. Mollura,et al. Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology. , 2020, Radiology.
[37] Vinod K. Sharma,et al. A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases , 2020, Journal of the European Academy of Dermatology and Venereology : JEADV.
[38] S. H. Regli,et al. Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer. , 2020, JAMA oncology.
[39] Q. Sun,et al. Genetic factors associated with cancer racial disparity – an integrative study across twenty‐one cancer types , 2020, Molecular oncology.
[40] M. Amgad,et al. High expression of MKK3 is associated with worse clinical outcomes in African American breast cancer patients , 2020, Journal of translational medicine.
[41] Hema Sekhar Reddy Rajula,et al. Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment , 2020, Medicina.
[42] James A. Anderson,et al. Clinical research underlies ethical integration of healthcare artificial intelligence , 2020, Nature Medicine.
[43] Zodwa Dlamini,et al. Artificial intelligence (AI) and big data in cancer and precision oncology , 2020, Computational and structural biotechnology journal.
[44] Shannon M. Lynch,et al. The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer , 2020, PloS one.
[45] A. M. López,et al. Cancer Disparities and Health Equity: A Policy Statement From the American Society of Clinical Oncology. , 2020, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[46] Courosh Mehanian,et al. Deep learning-based image evaluation for cervical precancer screening with a smartphone targeting low resource settings – Engineering approach , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
[47] Y. Qiao,et al. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence , 2020, BMC Medicine.
[48] Katherine Y Tossas,et al. Abstract A010: Hidden figures – an example of using machine learning to prioritize cervical cancer screening outreach , 2020 .
[49] E. Di Ruggiero,et al. Artificial intelligence for good health: a scoping review of the ethics literature , 2020, BMC medical ethics.
[50] Jia Wu,et al. Auxiliary Medical Decision System for Prostate Cancer Based on Ensemble Method , 2020, Comput. Math. Methods Medicine.
[51] E. Mema,et al. The Role of Artificial Intelligence in Understanding and Addressing Disparities in Breast Cancer Outcomes , 2020, Current Breast Cancer Reports.
[52] L. R. Long,et al. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera , 2020, International journal of cancer.
[53] Lauren Wilcox,et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy , 2020, CHI.
[54] Daniel B. Neill,et al. Big Data Analytics and the Struggle for Equity in Health Care: The Promise and Perils , 2020, Health equity.
[55] Y. Natkunam,et al. Low-cost transcriptional diagnostic to accurately categorize lymphomas in low- and middle-income countries. , 2020, Blood advances.
[56] D. Whiteman,et al. Evaluation of Sex-Specific Incidence of Melanoma. , 2020, JAMA dermatology.
[57] Ankita Kar,et al. Improvement of oral cancer screening quality and reach: The promise of Artificial Intelligence. , 2020, Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology.
[58] I. Olver,et al. The Use of Health-Related Technology to Reduce the Gap Between Developed and Undeveloped Regions Around the Globe. , 2020, American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting.
[59] B. Ilhan,et al. Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence , 2020, Journal of dental research.
[60] Ezio Di Nucci,et al. Concordance as evidence in the Watson for Oncology decision-support system , 2020, AI & SOCIETY.
[61] Yulong Tian,et al. Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study , 2020, Journal of medical Internet research.
[62] Jia Wu,et al. An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries , 2020, Comput. Math. Methods Medicine.
[63] Yan Gao,et al. Deep transfer learning for reducing health care disparities arising from biomedical data inequality , 2020, Nature Communications.
[64] Marzyeh Ghassemi,et al. Treating health disparities with artificial intelligence , 2020, Nature Medicine.
[65] Melonie P. Heron,et al. Healthy People 2020: Rural Areas Lag In Achieving Targets For Major Causes Of Death. , 2019, Health affairs.
[66] Rifat Atun,et al. Artificial intelligence and algorithmic bias: implications for health systems , 2019, Journal of global health.
[67] Ahmed Hosny,et al. Artificial intelligence for global health , 2019, Science.
[68] N. Glasgow,et al. Relative importance of clinical and sociodemographic factors in association with post-operative in-hospital deaths in colorectal cancer patients in New South Wales: An artificial neural network approach. , 2019, Journal of evaluation in clinical practice.
[69] Y. Natkunam,et al. High Accuracy, Low-Cost Transcriptional Diagnostic to Transform Lymphoma Care in Low- and Middle-Income Countries , 2019, Blood.
[70] Anne Marie Piper,et al. Deconstructing Community-Based Collaborative Design , 2019, Proc. ACM Hum. Comput. Interact..
[71] Sonia Allan,et al. A governance model for the application of AI in health care , 2019, J. Am. Medical Informatics Assoc..
[72] Brian W. Powers,et al. Dissecting racial bias in an algorithm used to manage the health of populations , 2019, Science.
[73] M. Steinberg,et al. Racial Disparity in the Genomic Basis of Radiosensitivity – An Exploration of Whole-Transcriptome Sequencing Data via a Machine-Learning Approach , 2019, International Journal of Radiation Oncology*Biology*Physics.
[74] Leo Anthony Celi,et al. The “inconvenient truth” about AI in healthcare , 2019, npj Digital Medicine.
[75] Dallin S Akagi,et al. Machine learning ensemble models predict total charges and drivers of cost for transsphenoidal surgery for pituitary tumor. , 2019, Journal of neurosurgery.
[76] David A. Chambers,et al. Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health? , 2019, Public Health Genomics.
[77] A. Carraro,et al. Abstract 4223: Conducting community oral cancer screening among South Asians in British Columbia , 2019, Prevention, Early Detection, and Interception.
[78] Cesar M. Castro,et al. Harnessing artificial intelligence and digital diffraction to advance point-of-care HPV 16 and 18 detection , 2019, Gynecologic Oncology.
[79] J. McDaniel,et al. Social determinants of cancer incidence and mortality around the world: an ecological study , 2019, Global health promotion.
[80] Jie Ma,et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. , 2019, Journal of clinical epidemiology.
[81] M. Ghassemi,et al. Can AI Help Reduce Disparities in General Medical and Mental Health Care? , 2019, AMA journal of ethics.
[82] M. Shew,et al. Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer , 2019, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.
[83] Jia Xu,et al. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives , 2019, Human Genetics.
[84] Alex John London,et al. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.
[85] G. Keerthi,et al. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities , 2018, PloS one.
[86] A. Adamson,et al. Machine Learning and Health Care Disparities in Dermatology. , 2018, JAMA dermatology.
[87] Jun Deng,et al. A multi-parameterized artificial neural network for lung cancer risk prediction , 2018, PloS one.
[88] Ben J. Marafino,et al. Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk , 2018, AIES.
[89] N. Couldry,et al. Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject , 2018, Television & New Media.
[90] S. Love,et al. Palpable Breast Lump Triage by Minimally Trained Operators in Mexico Using Computer-Assisted Diagnosis and Low-Cost Ultrasound , 2018, Journal of global oncology.
[91] Akira Hara,et al. A Review of HPV-Related Head and Neck Cancer , 2018, Journal of clinical medicine.
[92] Cesar M. Castro,et al. Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning , 2018, Nature Biomedical Engineering.
[93] Bishal Gyawali,et al. Does global oncology need artificial intelligence? , 2018, The Lancet. Oncology.
[94] B. Gyawali,et al. Cancer groundshot: going global before going to the moon. , 2018, The Lancet. Oncology.
[95] C. Lehman. Abstract IS-3: Breast Imaging in Resource Constrained Regions: Lessons from Uganda , 2018 .
[96] Michael Veale,et al. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data , 2017, Big Data Soc..
[97] Choudhary Shyam Prakash,et al. 1675PRisk of recurrence prediction and optimum treatment planning for early stage breast cancer patients: A cost-effective, accurate and broad based solution for Asia , 2017 .
[98] B. Olusanya,et al. Obligations under global health partnerships in LMICs should be contractual. , 2017, The Lancet. Global health.
[99] Dallin S Akagi,et al. The Impact of Race on Discharge Disposition and Length of Hospitalization After Craniotomy for Brain Tumor. , 2017, World neurosurgery.
[100] Judy Wajcman,et al. Automation: is it really different this time? , 2017, The British journal of sociology.
[101] Nancy Mayo,et al. Where have all the pilot studies gone? A follow-up on 30 years of pilot studies in Clinical Rehabilitation , 2017, Clinical rehabilitation.
[102] Sebastian Thrun,et al. Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.
[103] A. Andermann. Taking action on the social determinants of health in clinical practice: a framework for health professionals , 2016, Canadian Medical Association Journal.
[104] S. Dawsey,et al. A tablet-interfaced high-resolution microendoscope with automated image interpretation for real-time evaluation of esophageal squamous cell neoplasia. , 2016, Gastrointestinal endoscopy.
[105] Gypsyamber D'Souza,et al. HPV-associated head and neck cancer: a virus-related cancer epidemic. , 2010, The Lancet. Oncology.
[106] R. Meyer,et al. Moderate predictive value of demographic and behavioral characteristics for a diagnosis of HPV16-positive and HPV16-negative head and neck cancer. , 2010, Oral oncology.
[107] K. Ahluwalia,et al. Assessing the oral cancer risk of South‐Asian immigrants in New York City , 2005, Cancer.
[108] Matthew R Anderson,et al. Validity of racial/ethnic classifications in medical records data: an exploratory study. , 2003, American journal of public health.
[109] S. Singh,et al. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check , 2020, Euroasian journal of hepato-gastroenterology.
[110] P. Gillies. Effectiveness of Alliances and Partnerships for Health Promotion , 1998 .