HarrisRPSheridanSLLewisCL, et al. The harms of screening: a proposed taxonomy and application to lung cancer screening. JAMA Intern Med. 2014;174(2):281-285. doi:10.1001/jamainternmed.2013.12745
2.
BandiPStarJMazzitelliN, et al. Prevalence and review of major modifiable cancer risk factors, HPV vaccination, and cancer screenings in the United States: 2025 update. Cancer Epidemiol Biomarkers Prev. 2025;34(6):836-849. doi:10.1158/1055-9965.Epi-24-1835
3.
SedaniAEGomezSLLawrenceWRMooreJXBrandtHMRogersCR.Social risks and nonadherence to recommended cancer screening among US adults. JAMA Netw Open. 2025;8(1):e2449556. doi:10.1001/jamanetworkopen.2024.49556
4.
AlcarazKIWiedtTLDanielsECYabroffKRGuerraCEWenderRC.Understanding and addressing social determinants to advance cancer health equity in the United States: a blueprint for practice, research, and policy. CA Cancer J Clin. 2020;70(1):31-46. doi:10.3322/caac.21586
5.
JiangSBennettDLColditzGA.Validation of a dynamic risk prediction model incorporating prior mammograms in a diverse population. JAMA Netw Open. 2025;8(6):e2512681. doi:10.1001/jamanetworkopen.2025.12681
6.
WentzensenNLahrmannBClarkeMA, et al. Accuracy and efficiency of deep-learning–based automation of dual stain cytology in cervical cancer screening. J Natl Cancer Inst. 2021;113(1):72-79. doi:10.1093/jnci/djaa066
7.
HuLBellDAntaniS, et al. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J Natl Cancer Inst. 2019;111(9):923-932. doi:10.1093/jnci/djy225
8.
GaurSLayNHarmonSA, et al. Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? A multi-center, multi-reader investigation. Oncotarget. 2018;9(73):33804-33817. doi:10.18632/oncotarget.26100
9.
MikhaelPGWohlwendJYalaA, et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J Clin Oncol. 2023;41(12):2191-2200. doi:10.1200/JCO.22.01345
10.
YalaAMikhaelPGStrandF, et al. Toward robust mammography-based models for breast cancer risk. Sci Transl Med. 2021;13(578):eaba4373. doi:10.1126/scitranslmed.aba4373
HassanCSpadacciniMIannoneA, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021;93(1):77-85.e6. doi:10.1016/j.gie.2020.06.059
13.
DiamondCJLaurentievJYangJ, et al. Natural language processing to identify abnormal breast, lung, and cervical cancer screening test results from unstructured reports to support timely follow-up. Stud Health Technol Inform. 2022;290:433-437. doi:10.3233/shti220112
14.
LiuSMcCoyABAldrichMC, et al. Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record. Int J Med Inform. 2023;177:105136. doi:10.1016/j.ijmedinf.2023.105136
15.
YildirimNZlotnikovSSayarD, et al. Sketching AI concepts with capabilities and examples: AI innovation in the intensive care unit. Proc SIGCHI Conf Hum Factor Comput Syst. 2024;2024:451. doi:10.1145/3613904.3641896
16.
AdamsonASSmithA.Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018;154(11):1247-1248. doi:10.1001/jamadermatol.2018.2348
17.
ObermeyerZPowersBVogeliCMullainathanS.Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342
18.
Dankwa-MullanIWeeraratneD.Artificial intelligence and machine learning technologies in cancer care: addressing disparities, bias, and data diversity. Cancer Discov. 2022;12(6):1423-1427. doi:10.1158/2159-8290.CD-22-0373
19.
ElwynGRyanPBlumkinDWeeksWB.Meet generative AI . . . your new shared decision-making assistant. BMJ Evid Based Med. 2024;29(5):292-295. doi:10.1136/bmjebm-2023-112651
20.
FiskeARadhuberIMWillemTBuyxACeliLAMcLennanS.Climate change and health: the next challenge of ethical AI. Lancet Glob Health. 2025;13(7):e1314-e1320. doi:10.1016/S2214-109X(25)00124-X