In current medical practice when a patient feels symptoms he/she would consult the doctor. The doctor then gives medication in a one-fits-all fashion. However, recent genetics studies had shown that different genetic makeup can results in different effects on medication, so the medication should be customed for every individual. The main idea of “personalized medicine” is to provide the right intervention including medication to the right patient at the right time and dose. With this approach, the medication paradigm would shift from curative to preventive. The rise of personalized medicine had been possible because the information from ever-increasing biomolecular (proteomics, genomics, and other omics) and health-related data are successfully “mined” by Artificial Intelligence (AI) tools. In this paper, we proposed that AI systems toward personalized medicine must have acceptable performance, be readily interpretable by the clinical community, and be validated in a large cohort. We examined a few landmark papers with the keyword “AI for personalized medicine application”; 1) automatic image-based patient classification, 2) automatic gene-based cancer classification, and 3) automatic health-record heart failure with preserved ejection fraction patient phenotyping. All the examples are evaluated by their performance, interpretability, and clinical validity. From the analysis, we concluded that AI for personalized medicine could benefit by five factors: (1) standardization and pooling of genetics and health data, nationally and internationally, (2) the use of multi-modalities data, (3) disease specialist to guide the development of AI model, (4) investigation of AI-finding by clinical community, and (5) follow-up of AI-finding by the large clinical trial.


Abul-husn, N. S., & Kenny, E. E. (2019). Perspective Personalized Medicine and the Power of Electronic Health Records. Cell, 177(1), 58–69. https://doi.org/10.1016/j.cell.2019.02.039

Agerbo, E., Sullivan, P. F., Vilhjalmsson, B. J., & Pedersen, C. B. (2015). Polygenic Risk Score, Parental Socioeconomic Status, Family History of Psychiatric Disorders, and the Risk for Scizophrenia: A Danish Population-Based Study and Meta Analysis. Jama Pscychiatry, 72, 635–641.

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular Pharmaceutics, 13(7), 2524–2530. https://doi.org/10.1021/acs.molpharmaceut.6b00248

Athreya, A., Iyer, R., Neavin, D., Wang, L., Weinshilboum, R., Kaddurah-Daouk, R., … Bobo, W. (2018). Augmentation of physician assessments with multi-omics enhances predictability of drug response: A case study of major depressive disorder. IEEE Computational Intelligence Magazine, 13(3), 20–31. https://doi.org/10.1109/MCI.2018.2840660

Beck, A. H., van de Rijn, M., van de Vijver, M. J., Sangoi, A. R., Koller, D., Marinelli, R. J., … Nielsen, T. O. (2011). Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science Translational Medicine, 3(108), 108ra113-108ra113. Retrieved from http://stm.sciencemag.org/cgi/doi/10.1126/scitranslmed.3002564%5Cnfile:///Files/34/34AF3276-62E1-4587-B757-ECB6DA0E3FB9.pdf%5Cnpapers3://publication/doi/10.1126/scitranslmed.3002564

Bianchini, G., Qi, Y., Alvarez, R. H., Iwamoto, T., Coutant, C., Ibrahim, N. K., … Pusztai, L. (2010). Molecular Anatomy of Breast Cancer Stroma and Its Prognostic Value in Estrogen Receptor – Positive and – Negative Cancers. Journal of Clinical Oncology, 28, 4316–4323. https://doi.org/10.1200/JCO.2009.27.2419

Blackstone, E. H. (2019). Precision Medicine Versus Evidence-Based Medicine: Individual Treatment Effect Versus Average Treatment Effect. Circulation, 140(15), 1236–1238. https://doi.org/10.1161/CIRCULATIONAHA.119.043014

Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., … Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-X

Chen, Z., Chen, J., Collins, R., Guo, Y., Peto, R., Wu, F., & Li, L. (2011). China Kadoorie Biobank of 0 . 5 million people : survey methods , baseline characteristics and long-term follow-up. International Journal of Epidemiology, 40(September), 1652–1666. https://doi.org/10.1093/ije/dyr120

Cheng, W., Yang, T. O., & Anastassiou, D. (2013). Biomolecular Events in Cancer Revealed by Attractor Metagenes. Plos Computational Biology, 9(2). https://doi.org/10.1371/journal.pcbi.1002920

Clayton, T. A., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G., … Nicholson, J. K. (2006). Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 440(7087), 1073–1077. https://doi.org/10.1038/nature04648

Collins, R. (2012). What Makes UK Biobank special? Lancet, 379, 1173–1174.

Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., … Van Westen, G. J. P. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202–1212. https://doi.org/10.1038/nbt.2877

Deo, R. C. (2015). Machine Learning in Medicine. Circulation, 132(20), 1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593.Machine

Ding, Z., Zu, S., & Gu, J. (2016). Evaluating the molecule-based prediction of clinical drug responses in cancer. Bioinformatics, 32(19), 2891–2895. https://doi.org/10.1093/bioinformatics/btw344

Fanshawe, T. R., Lynch, A. G., Ellis, I. O., Green, A. R., & Hanka, R. (2008). Assessing Agreement between Multiple Raters with Missing Rating Information , Applied to Breast Cancer Tumour Grading. PLoS ONE, 3(8). https://doi.org/10.1371/journal.pone.0002925

Finak, G., Bertos, N., Pepin, F., Sadekova, S., Souleimanova, M., Zhao, H., … Park, M. (2008). Stromal gene expression predicts clinical outcome in breast cancer. Nature Medicine, 14(April), 518–527. https://doi.org/10.1038/nm1764

Frankell, A. M., Jammula, S. G., Li, X., Contino, G., Killcoyne, S., Abbas, S., … Fitzgerald, R. C. (2019). The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic. Nature Genetics, 51(3), 506–516. https://doi.org/10.1038/s41588-018-0331-5

Fröhlich, H., Balling, R., Beerenwinkel, N., Kohlbacher, O., Kumar, S., Lengauer, T., … Zupan, B. (2018). From hype to reality: data science enabling personalized medicine. BMC Medicine, 16(1), 150. https://doi.org/10.1186/s12916-018-1122-7

Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178(11), 1544–1547. https://doi.org/10.1001/jamainternmed.2018.3763.Potential

Gottesman, O., Kuivaniemi, H., Tromp, G., Faucett, W. A., Li, R., Manolio, T. A., … Network, T. (2013). Open The Electronic Medical Records and Genomics (eMERGE) Network : past , present , and future. Genetics in Medicine, 15(10), 761–771. https://doi.org/10.1038/gim.2013.72

Hoffman, M. A., & Williams, M. S. (2011). Electronic medical records and personalized medicine. Human Genetics, 130, 33–39.

Holmes, M. V, Perel, P., Shah, T., Hingorani, A. D., Casa, J. P., Hoskins, J. M., … Weinshilboum, R. M. (2011). Use of omeprazole as a probe drug for CYP2C19 phenotype in Swedish Caucasians: comparison with S-mephenytoin hydroxylation phenotype and CYP2C19 genotype. Pharmacogenetics and Genomics, 5(6), 335–398.

Huopaniemi, I., Nadkarni, G., Nadukuru, R., Lotay, V., Ellis, S., Gottesman, O., & Bottinger, E. P. (2014). Disease progression subtype discovery from longitudinal EMR data with a majority of missing values and unknown initial time points. In AMIA Annual Symposium Proceedings (pp. 709–718).

Ingelsson, E., & McCarthy, M. I. (2018). Human Genetics of Obesity and Type 2 Diabetes Mellitus. Circulation: Genomic and Precision Medicine, 11(June), 1–12. https://doi.org/10.1161/CIRCGEN.118.002090

Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13, 395–405.

Kensler, T. W., Spira, A., Garber, J. E., Szabo, E., Lee, J. J., Dong, Z., … Lippman, S. M. (2016). Transforming cancer prevention through precision medicine and immune-oncology. Cancer Prevention Research, 9(1), 2–10. https://doi.org/10.1158/1940-6207.CAPR-15-0406

Mak, A. C. Y., White, M. J., Eckalbar, W. L., Szpiech, Z. A., Oh, S. S., Pino-yanes, M., … Hernandez, R. D. (2018). Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma. American Journal of Respiratory and Critical Care Medicine, 197(12), 1552–1564. https://doi.org/10.1164/rccm.201712-2529OC

Mulligan, G., Mitsiades, C., Bryant, B., Zhan, F., Chng, W. J., Roels, S., … Anderson, K. C. (2007). Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Blood, 109(8), 3177–3188. https://doi.org/10.1182/blood-2006-09-044974

Nagai, A., Hirata, M., Kamatani, Y., Muto, K., & Matsuda, K. (2017). Overview of the BioBank Japan Project : Study design and pro fi le. Journal of Epidemiology, 27, S2–S8. https://doi.org/10.1016/j.je.2016.12.005

Nguyen, T. T. L., Liu, D., Ho, M. F., Athreya, A. P., & Weinshilboum, R. (2021). Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction. Frontiers in Pharmacology, 11(January), 1–10. https://doi.org/10.3389/fphar.2020.614048

Nichols, J. A., Herbert Chan, H. W., & Baker, M. A. B. (2019). Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophysical Reviews, 11(1), 111–118. https://doi.org/10.1007/s12551-018-0449-9

Patey, D. H., & Scarff, R. W. (1928). The position of histology in the prognosis of carcinoma of the breast. Lancet, 801–804.

Roche-Lima, A., Roman-Santiago, A., Feliu-Maldonado, R., Rodriguez-Maldonado, J., Nieves-Rodriguez, B. G., Carrasquillo-Carrion, K., … Duconge, J. (2020). Machine learning algorithm for predicting warfarin dose in caribbean hispanics using pharmacogenetic data. Frontiers in Pharmacology, 10(January), 1–8. https://doi.org/10.3389/fphar.2019.01550

Sakellaropoulos, T., Vougas, K., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., … Gorgoulis, V. G. (2019). A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Reports, 29(11), 3367-3373.e4. https://doi.org/10.1016/j.celrep.2019.11.017

Shah, S. J., Katz, D. H., Selvaraj, S., Burke, M. A., Yancy, C. W., Gheorghiade, M., & Bonow, R. O. (2015). Phenomapping for Novel Classification of Heart Failure with Preserved Ejection Fraction. Circulation, 131(3), 269–279. https://doi.org/10.1161/CIRCULATIONAHA.114.010637.Phenomapping

Shendure, J., & Ji, H. (2008). Next-generation DNA sequencing. Nature Biotechnology, 26, 1135–1145.

Singer, J., Irmisch, A., Ruscheweyh, H. J., Singer, F., Toussaint, N. C., Levesque, M. P., … Beerenwinkel, N. (2017). Bioinformatics for precision oncology. Briefings in Bioinformatics, 20(3), 778–788. https://doi.org/10.1093/bib/bbx143

Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., … Collins, R. (2015). UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Medicine, 12(3), 1–10. https://doi.org/10.1371/journal.pmed.1001779

Tsimberidou, A. M., Fountzilas, E., Nikanjam, M., & Kurzrock, R. (2020). Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treatment Reviews. https://doi.org/10.1016/j.ctrv.2020.102019

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., … Garnett, M. J. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research, 41(D1), 955–961. https://doi.org/10.1093/nar/gks1111

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/https://doi.org/10.1111/j.1467-9868.2005.00503.x



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