Data bias in precision medicine
DOI:
https://doi.org/10.18203/2349-3933.ijam20223030Keywords:
Precision medicine, AI, ML, Data biasAbstract
Precision medicine is poised to increasingly improve health outcomes for more people in the near future. In contrast to the more traditional reactive methods of disease treatment, precision medicine is a customizable treatment and disease prevention approach that is tailored for the individual. Artificial intelligence (AI) using sophisticated algorithms and machine learning (ML) tools powers these precision medicine processes. These algorithms analyze big data collected from multiple sources over the past decades to aid physicians to make data-backed critical clinical decisions. However, studies have shown that unintentional biases in the source data and in the process can affect these precision medicine efforts.
References
Ashley EA. Towards precision medicine. Nature Rev Genetics. 2016;17(9):507-22.
Prosperi M, Min JS, Bian J, Modave F. Big data hurdles in precision medicine and precision public health. BMC Med Informatics Decision Making. 2018;18(1):1-15.
Schaefer GO, Tai ES, Sun S. Precision Medicine and Big Data. Asian Bioethics Rev. 2019;11(3):275-88.
Mesko B. The role of artificial intelligence in precision medicine. Expert Rev Precision Med Drug Develop. 2017;2(5):239-41.
Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The need for multi-omics biomarker signatures in precision medicine. Int J Molecular Sci. 2019;20(19):4781.
Cammarota G, Ianiro G, Ahern A, Carbone C, Temko A, Claesson MJ et al. Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nature Rev Gastroenterol Hepatol. 2020;17(10):635-48.
Leff DR, Yang GZ. Big data for precision medicine. Engineering. 2015;1(3):277-9.
Chen R, Snyder M. Promise of personalized omics to precision medicine. Wiley Interdisciplinary Rev: Systems Biol Med. 2013;5(1):73-82.
Kornelia B, Ślęzak A. The use of Big Data Analytics in healthcare. J Big Data. 2022;9(1).
Cirillo D, Valencia A. Big data analytics for personalized medicine. Curr Opinion Biotechnol. 2022;58:161-7.
Ristevski B, Chen M. Big data analytics in medicine and healthcare. J Integrative Bioinformatics. 2018;15(3).
Mayer‐Schönberger V, Ingelsson E. Big Data and medicine: a big deal? J Internal Med. 2018;283(5):418-29.
Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ. Big data for health. IEEE J Biomed Heal Informatics. 2018;19(4):1193-208.
Panch T, Mattie H, Atun R. Artificial intelligence and algorithmic bias: implications for health systems. J Glob Heal. 2019;9(2).
Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns. 2021;2(10):100347.
Sjoding MW, Dickson RP, Iwashyna TJ, Gay SE, Valley TS. Racial bias in pulse oximetry measurement. N Eng J Med. 2020;383(25):2477-8.
Vesoulis Z, Tims A, Lodhi H, Lalos N, Whitehead H. Racial discrepancy in pulse oximeter accuracy in preterm infants. J Perinatol. 2022;42(1):79-85.
Tasci E, Zhuge Y, Camphausen K, Krauze AV. Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets. Cancers. 2022;14(12):2897.
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53.
Martin AR, Stroud RE II, Abebe T, Akena D, Alemayehu M, Atwoli L et al. Increasing diversity in genomics requires investment in equitable partnerships and capacity building. Nature Genetics. 2022;54(6):740-5.
Lee SS, Fullerton SM, McMahon CE, Bentz M, Saperstein A, Jeske M et al. Targeting Representation: Interpreting Calls for Diversity in Precision Medicine Research. Yale J Biol Med. 2022;95(3):317-26.
Huang J, Galal G, Etemadi M, Vaidyanathan M. Evaluation and mitigation of racial bias in clinical machine learning models: Scoping review. JMIR Med Informatics. 2022;10(5).