The impact of artificial intelligence on health equity in oncology: A scoping review (Preprint)

[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 .