1. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endoscop 2020;92:807-12. [DOI:10.1016/j.gie.2020.06.040]

2. Aronson SJ, Rehm HL. Building the foundation for genomics in precision medicine. Nature 2015;526. [DOI:10.1038/nature15816]

3. Makary MA, Daniel M. Medical error-the third leading cause of death in the US. BMJ 2016;353:i2139. [DOI:10.1136/bmj.i2139]

4. Sboner A, Elemento O. A primer on precision medicine informatics. Brief Bioinform 2016;17. [DOI:10.1093/bib/bbv032]

5. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019;25. [DOI:10.1038/s41591-018-0307-0]

6. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA 2017;318:2199-210. [DOI:10.1001/jama.2017.14585]

7. Bello GA, Dawes TJW, Duan J, Biffi C, de Marvao A, Howard L, et al. Deep learning cardiac motion analysis for human survival prediction. Nat Mach Intell 2019;1:95-104. [DOI:10.1038/s42256-019-0019-2]

8. Swapna M, Viswanadhula UM, Aluvalu R, Vardharajan V, Kotecha K. Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence. Journal of Sensor and Actuator Networks 2022;11:17. 9. Vashistha R, Dangi AK, Kumar A, Chhabra D, Shukla P. Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech 2018;8:358. https://doi.org/10.1007/s13205-018-1368-y 10. Mehrotra P. Biosensors and their applications - a review. J Oral Biol Craniofac Res 2016;6:153-9. https://doi.org/10.1016/j.jobcr.2015.12.002 11. Jin X, Liu C, Xu T, Su L, Zhang X. Artificial intelligence biosensors: Challenges and prospects. Biosens Bioelectron 2020;165:112412. https://doi.org/10.1016/j.bios.2020.112412 12. Gupta RR. Application of Artificial Intelligence and Machine Learning in Drug Discovery. Methods Mol Biol 2022; 2390:113-124. https://doi.org/10.1007/978-1-0716-1787-8_4 13. Chopra H, Baig AA, Gautam RK, Kamal MA. Application of Artificial Intelligence in Drug Discovery. Curr Pharmaceut Design 2022;28:2690-703. https://doi.org/10.2174/1381612828666220608141049 [DOI:10.3390/jsan11010017]

9. Farghali H, Kutinová Canová N, Arora M. The potential applications of artificial intelligence in drug discovery and development. Physiol Res 2021;70:S715-22. [DOI:10.33549/physiolres.934765]

10. Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov 2021;16:949-59. [DOI:10.1080/17460441.2021.1909567]

11. van Hartskamp M, Consoli S, Verhaegh W, Petkovic M, van de Stolpe A. Artificial Intelligence in Clinical Health Care Applications: Viewpoint. Interact J Med Res 2019;8:e12100. [DOI:10.2196/12100]

12. Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology-promises and pitfalls. Am J Gastroenterol 2019;114:422-8. [DOI:10.1038/s41395-018-0268-4]

13. Greenhill AT, Edmunds BR. A Primer of AI in Medicine. Techniques in Gastrointestinal Endoscopy. 2019; 22: 85-89. 19. Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. A primer on deep learning in genomics. Nat Genet 2019;51. https://doi.org/10.1038/s41588-018-0295-5 20. Kaur P, Sharma M, Mittal M. Big data and machine learning-based secure healthcare framework. Procedia Comput Sci 2018;132. https://doi.org/10.1016/j.procs.2018.05.020 21. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018;178. https://doi.org/10.1001/jamainternmed.2018.3763 22. Quazi S. A vaccine in response to COVID-19: Recent developments, challenges, and a way out. Biomedical and Biotechnology Research Journal (BBRJ) 2021;5:105-109. https://doi.org/10.4103/bbrj.bbrj_166_20 23. Cho G, Yim J, Choi Y, Ko J, Lee SH. Review of Machine Learning Algorithms for Diagnosing Mental Illness. Psychiatry Investig 2019;16:262-69. https://doi.org/10.30773/pi.2018.12.21.2 24. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836-42. https://doi.org/10.1093/annonc/mdy166 25. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate ***deep learning with electronic health records. NPJ Digital Medicine. 2018;1:18. https://doi.org/10.1038/s41746-018-0029-1 26. Xu W, Zhao Y, Nian S, Feng L, Bai X, Luo X, et al. Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies. J Int Med Res 2018;46: 3656-3664. https://doi.org/10.1177/0300060518777173 27. Mandelkow H, Zwart JA, Duyn JH. Linear discriminant analysis achieves high classification accuracy for the BOLD fMRI response to naturalistic movie stimuli. Front Hum Neurosci 2016;10:128. https://doi.org/10.3389/fnhum.2016.00128 28. Tsai WM, Zhang H, Buta E, O'Malley S, Gueorguieva R. A modified classification tree method for personalised medical decisions. Stat Interface 2016;9:239-53. https://doi.org/10.4310/SII.2016.v9.n2.a11 29. Chern CC, Chen YJ, Hsiao B. Decision tree-based classifier in providing telehealth service. BMC Med Inform Decis Mak 2019;19:104. https://doi.org/10.1186/s12911-019-0825-9 30. Lee J. Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill. JMIR medical informatics. 2017;5:e3. https://doi.org/10.2196/medinform.6690 31. DuBrava S, Mardekian J, Sadosky A, Bienen EJ, Parsons B, Hopps M, et al. Using random forest models to identify correlates of a diabetic peripheral neuropathy diagnosis from electronic health record data. Pain Med 2017;18:107-15. https://doi.org/10.1093/pm/pnw096 32. Madadizadeh F, Asar ME, Bahrampour A. Quantile regression and its crucial role in promoting medical research. Iran J Public Health 2016;45:116-17. 33. Doing-Harris K, Mowery DL, Daniels C, Chapman WW, Conway M. Understanding patient satisfaction with received healthcare services: a natural language processing approach. AMIA Annu Symp Proc 2017;2016:524-33. 34. Wagholikar KB, Vijayraghavan S, Deshpande AW. Fuzzy Naive Bayesian model for medical diagnostic decision support. Annu Int Conf IEEE Eng Med Biol Soc 2009;2009:3409-12. https://doi.org/10.1109/IEMBS.2009.5332468 35. Vitola J, Pozo F, Tibaduiza DA, Anaya M. A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications. Sensors (Basel) 2017;17:417. https://doi.org/10.3390/s17020417 36. Esmaili N, Piccardi M, Kruger B, Girosi F. Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models. PLoS One 2019;14:e0214973. https://doi.org/10.1371/journal.pone.0214973 37. Huttunen MJ, Hassan A, McCloskey CW, Fasih S, Upham J, Vanderhyden BC, et al. Automated classification of multiphoton microscopy images of ovarian tissue using deep learning. J Biomed Optics 2018;23:1-7. https://doi.org/10.1117/1.JBO.23.6.066002 38. Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer 2019;119:11-7. https://doi.org/10.1016/j.ejca.2019.05.023 39. Kaseb AO, Sánchez NS, Sen S, Kelley RK, Tan B, Bocobo AG, et al. Molecular Profiling of Hepatocellular Carcinoma Using Circulating Cell-Free DNA. Clin Cancer Res 2019;25:6107-18. https://doi.org/10.1158/1078-0432.CCR-18-3341 40. Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 2005;34:113-27. https://doi.org/10.1016/j.artmed.2004.07.002 41. Jović S, Miljković M, Ivanović M, Šaranović M, Arsić M. Prostate cancer probability prediction by machine learning technique. Cancer Invest. 2017;35:647-51. https://doi.org/10.1080/07357907.2017.1406496 42. Lynch CM, Abdollahi B, Fuqua JD, Alexandra R, Bartholomai JA, Balgemann RN, et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inform 2017;108:1-8. https://doi.org/10.1016/j.ijmedinf.2017.09.013 43. Lu CF, Hsu FT, Hsieh KLC, Kao YCJ, Cheng SJ, Hsu JBK. Machine learning-based radionics for molecular subtyping of gliomas. Clin Cancer Res 2018;24:4429-36. https://doi.org/10.1158/1078-0432.CCR-17-3445 44. Mallavarapu T, Hao J, Kim Y, Oh JH, Kang M. Pathway-based deep clustering for molecular subtyping of cancer. Methods 2020;173:24-31. https://doi.org/10.1016/j.ymeth.2019.06.017 45. Zhao M, Tang Y, Kim H, Hasegawa K. Machine learning with k-means dimensional reduction for predicting survival outcomes in patients with breast cancer. Cancer Inform 2018;17: 1176935118810215. https://doi.org/10.1177/1176935118810215 46. Azuaje F, Kim SY, Perez Hernandez D, Dittmar G. Connecting histopathology imaging and proteomics in kidney cancer through machine learning. J Clin Med 2019;8: 1535. https://doi.org/10.3390/jcm8101535 47. Li H, Siddiqui O, Zhang H, Guan Y. Cooperative learning improves protein abundance prediction in cancers. BMC Biol 2019;17:107. https://doi.org/10.1186/s12915-019-0730-9 48. Stemke-Hale K, Gonzalez-Angulo AM, Lluch A, Neve RM, Kuo WL, Davies M, et al. An integrative genomic and proteomic analysis of PIK3CA, PTEN, and AKT mutations in breast cancer. Cancer Res 2008;68:6084-91. https://doi.org/10.1158/0008-5472.CAN-07-6854 49. Nam H, Chung BC, Kim Y, Lee K, Lee D. Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics 2009;25:3151-7. https://doi.org/10.1093/bioinformatics/btp558 50. Gao Q, Zhu H, Dong L, Shi W, Chen R, Song Z, et al. ntegrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell 2019;179:561-577.e22. https://doi.org/10.1016/j.cell.2019.08.052 51. Playford D, Bordin E, Talbot L, Mohamad R, Anderson B, Strange G. Analysis of aortic stenosis using artificial intelligence. Heart Lung and Circulation 2018;27:S216. https://doi.org/10.1016/j.hlc.2018.06.390 52. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol 2016;68:2287-95. https://doi.org/10.1016/j.jacc.2016.08.062 53. Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med 2018;1:6. https://doi.org/10.1038/s41746-017-0013-1 54. Ohta Y, Yunaga H, Kitao S, Fukuda T, Ogawa T. Detection and Classification of Myocardial Delayed Enhancement Patterns on MR Images with Deep Neural Networks: A Feasibility Study. Radiol Artif Intell 2019;1:e180061. https://doi.org/10.1148/ryai.2019180061 55. Tao Q, Yan W, Wang Y, Paiman EH, Shamonin DP, Garg P, et al. Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study. Radiology 2019;290:81-88. https://doi.org/10.1148/radiol.2018180513 56. Ngo TA, Lu Z, Carneiro G. Combining deep learning and level set for the automated segmentation of the heart's left ventricle from cardiac cine magnetic resonance. Med Image Anal 2017;35:159-71. https://doi.org/10.1016/j.media.2016.05.009 57. Kwon JM, Lee Y, Lee Y, Lee S, Park J. An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc 2018;7: e008678. https://doi.org/10.1161/JAHA.118.008678 58. Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, et al. Analysis of machine learning techniques for heart failure readmissions. Circulation 2016;9:629-40. https://doi.org/10.1161/CIRCOUTCOMES.116.003039 59. Alaa AM, Bolton T, Angelantonio E, Rudd JH, Schaar M. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS ONE 2019;14: e0213653. https://doi.org/10.1371/journal.pone.0213653 60. Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019;25:70-74. https://doi.org/10.1038/s41591-018-0240-2 61. Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 2019;20:389-403. https://doi.org/10.1038/s41576-019-0122-6 62. Turner AW, Wong D, Khan MD, Dreisbach CN, Palmore M, Miller CL. Multi-omics approaches to study extended non-coding RNA function in atherosclerosis. Front Cardiovasc Med 2019;6:9. https://doi.org/10.3389/fcvm.2019.00009 63. Burghardt TP, Ajtai K. Neural/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype. J Mol Cell Cardiol 2018;119:19-27. https://doi.org/10.1016/j.yjmcc.2018.04.006 64. Cazacu IM, Udristoiu A, Gruionu LG, Iacob A, Gruionu G, Saftoiu A. Artificial intelligence in pancreatic cancer: Toward precision diagnosis. Endoscopic Ultrasound 2019;8:357. https://doi.org/10.4103/eus.eus_76_19 65. Rotondano G, Cipolletta L, Grossi E, Koch M, Intraligi M, Buscema M, et al. Artificial neural networks accurately predict mortality in patients with nonvariceal upper GI bleeding. Gastrointest Endoscop 2011;73:218-26. https://doi.org/10.1016/j.gie.2010.10.006 66. Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, Watanabe G, et al. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer: Interdisciplinary Int J Am Cancer Soc 2005;103:1596-605. https://doi.org/10.1002/cncr.20938 67. Rustam F, Reshi AA, Mehmood A, Ullah S, On BW, Aslam W, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access 2020;8:101489-101499 . https://doi.org/10.1109/ACCESS.2020.2997311 68. Liu J, Xu H, Chen Q, Zhang T, Sheng W, Huang Q, et al. Prediction of hematoma expansion in spontaneous intracerebral haemorrhage using support vector machine. EBioMedicine 2019;43:454-59. https://doi.org/10.1016/j.ebiom.2019.04.040 69. Çınarer G, Emiroğlu BG, Ed. Classificatin of Brain Tumors by Machine Learning Algorithms. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); 2019, 11-13 Oct. https://doi.org/10.1109/ISMSIT.2019.8932878 [DOI:10.1016/j.tgie.2019.150642]

14. Dritsas E, Trigka M. Supervised Machine Learning Models for Liver Disease Risk Prediction. Computers 2023;12:19. [DOI:10.3390/computers12010019]

15. Ahmed S, Choi KY, Lee JJ, Kim BC, Kwon GR, Lee KH, et al. Ensembles of patch-based classifiers for diagnosis of Alzheimer disease. IEEE Access 2019;7:73373 - 73383. [DOI:10.1109/ACCESS.2019.2920011]

16. Kulkarni NN, Bairagi VK. Extracting salient features for EEG-based diagnosis of Alzheimer's disease using support vector machine classifier. IETE J Res 2017;63:11-22. [DOI:10.1080/03772063.2016.1241164]

17. Hariharan M, Polat K, Sindhu R. A new hybrid intelligent system for accurate detection of Parkinson's disease. Comput Methods Programs Biomed 2014;113:904-13. [DOI:10.1016/j.cmpb.2014.01.004]

18. Kousarrizi MN, Seiti F, Teshnehlab M. An experimental comparative study on thyroid disease diagnosis based on feature subset selection and classification. Int J Electr Comput Sci IJECS-IJENS. 2012;12.

19. Kumar D, Jain N, Khurana A, Mittal S, Satapathy SC, Senkerik R, et al. Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access. 2020;8142521 - 142531. [DOI:10.1109/ACCESS.2020.3012292]

20. Roth SC. What is genomic medicine? Med Libr Assoc 2019;107:442-8. [DOI:10.5195/jmla.2019.604]

21. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018;15: 20170387. [DOI:10.1098/rsif.2017.0387]

22. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med 2019;11:70. [DOI:10.1186/s13073-019-0689-8]

23. Li H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 2014;30:2843-51. [DOI:10.1093/bioinformatics/btu356]

24. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011;43:491-8. [DOI:10.1038/ng.806]

25. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv:1207.3907 [q-bio.GN] 2012.

26. Hwang S, Kim E, Lee I, Marcotte EM. Systematic comparison of variant calling pipelines using gold standard personal exome variants. Sci Rep 2015;5:175-78. [DOI:10.1038/srep17875]

27. Poplin R, Chang P-C, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 2018;36:983-7. [DOI:10.1038/nbt.4235]

28. Teng H, Cao MD, Hall MB, Duarte T, Wang S, Coin LJ. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning. GigaScience 2018;7:giy037. [DOI:10.1093/gigascience/giy037]

29. Chatterjee S, Ahituv N. Gene regulatory elements, major drivers of human disease. Annu Rev Genomics Hum Genet 2017;18:45-63. [DOI:10.1146/annurev-genom-091416-035537]

30. Soemedi R, Cygan KJ, Rhine CL, Wang J, Bulacan C, Yang J, et al. Pathogenic variants that alter protein code often disrupt splicing. Nat Genet 2017;49:848-55. [DOI:10.1038/ng.3837]

31. Baeza-Centurion P, Miñana B, Schmiedel JM, Valcárcel J, Lehner B. Combinatorial genetics reveals a scaling law for the effects of mutations on splicing. Cell 2019;176:549-63. [DOI:10.1016/j.cell.2018.12.010]

32. Jaganathan K, Panagiotopoulou SK, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting splicing from primary sequence with deep learning. Cell 2019;176:535-48. [DOI:10.1016/j.cell.2018.12.015]

33. Telenti A, Pierce LC, Biggs WH, Di Iulio J, Wong EH, Fabani MM, et al. Deep sequencing of 10,000 human genomes. Proc Natl Acad Sci U S A 2016;113:11901-6. [DOI:10.1073/pnas.1613365113]

34. Tang H, Thomas PD. Tools for predicting the functional impact of nonsynonymous genetic variation. Genetics 2016;203:635-47. [DOI:10.1534/genetics.116.190033]

35. Kircher M, Witten DM, Jain P, O'roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 2014;46:310-15. [DOI:10.1038/ng.2892]

36. Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 2014;31:761-3. [DOI:10.1093/bioinformatics/btu703]

37. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021;26:80-93. [DOI:10.1016/j.drudis.2020.10.010]

38. Gavas S, Quazi S, Karpiński TM. Nanoparticles for Cancer Therapy: Current Progress and Challenges. Nanoscale Res Lett 2021;16:173. [DOI:10.1186/s11671-021-03628-6]

39. Leenay RT, Aghazadeh A, Hiatt J, Tse D, Roth TL, Apathy R, et al. Large dataset enables prediction of repair after CRISPR-Cas9 editing in primary T cells. Nat Biotechnol 2019;37:1034-7. [DOI:10.1038/s41587-019-0203-2]

40. Shen MW, Arbab M, Hsu JY, Worstell D, Culbertson SJ, Krabbe O, et al. Predictable and precise template-free CRISPR editing of pathogenic variants. Nature 2018;563:646-51. [DOI:10.1038/s41586-018-0686-x]

41. Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R, et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Aacids Res 2017;45:D985-94. [DOI:10.1093/nar/gkw1055]

42. Pineda SS, Undheim EA, Rupasinghe DB, Ikonomopoulou MP, King GF. Spider venomics: Implications for drug discovery. Future Med Chem 2014;6:1699-714. [DOI:10.4155/fmc.14.103]

43. Li M, Zhang H, Chen B, Wu Y, Guan L. Prediction of pKa values for neutral and basic drugs based on hybrid artificial intelligence methods. Sci Rep 2018;8:3991. [DOI:10.1038/s41598-018-22332-7]

44. Amirinejad R, Shirvani-Farsani Z, Mohebbi S. The application of DNA-conjugated gold nanoparticles to detect metabolites and nucleic acids in personalized medicine. Personalized Medicine Journal 2021;6:23-5.

45. Lin M, Hu H, Zhou S, Xu S. Soft wearable devices for deep-tissue sensing. Nature Reviews Materials 2022;7:850-69. [DOI:10.1038/s41578-022-00427-y]

46. Belknap R, Weis S, Brookens A, Au-Yeung KY, Moon G, DiCarlo L, et al. Feasibility of an ingestible sensor-based system for monitoring adherence to tuberculosis therapy. PLoS One 2013;8:e53373. [DOI:10.1371/journal.pone.0053373]

47. Zhang Y, Hu Y, Jiang N, Yetisen AK. Wearable artificial intelligence biosensor networks. Biosens Bioelectron 2023;219:114825. [DOI:10.1016/j.bios.2022.114825]

48. Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today 2021;26:511-24. [DOI:10.1016/j.drudis.2020.12.009]

49. Vourvopoulos A, Niforatos E, Giannakos MN. EEGlass: an EEG-eyeware prototype for ubiquitous brain-computer interaction. Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers; 2019. [DOI:10.1145/3341162.3348383]

50. Nittala AS, Steimle J. Next Steps in Epidermal Computing: Opportunities and Challenges for Soft On-Skin Devices. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems; New Orleans, LA, USA: Association for Computing Machinery; 2022. [DOI:10.1145/3491102.3517668]

51. Wang Y, Yin L, Bai Y, Liu S, Wang L, Zhou Y, et al. Electrically compensated, tattoo-like electrodes for epidermal electrophysiology at scale. Sci Adv 2020;6:eabd0996. [DOI:10.1126/sciadv.abd0996]

52. Bandodkar AJ, Gutruf P, Choi J, Lee K, Sekine Y, Reeder JT, et al. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci Adv 2019;5:eaav3294. [DOI:10.1126/sciadv.aav3294]

53. Nittala AS, Karrenbauer A, Khan A, Kraus T, Steimle J. Computational design and optimization of electro-physiological sensors. Nat Commun 2021;12:6351. [DOI:10.1038/s41467-021-26442-1]

54. Dashtipour H, nouras A, daneshjou s, mohebbi S, mousaviniri N. Recent progress in (nano) biosensors: artificial intelligence (AI) application. Modares Journal of Biotechnology 2023;13:55-72.

55. Alfian G, Syafrudin M, Ijaz MF, Syaekhoni MA, Fitriyani NL, Rhee J. A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing. Sensors 2018;18:2183. [DOI:10.3390/s18072183]

56. Jeong H, Lee JY, Lee K, Kang YJ, Kim J-T, Avila R, et al. Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients. Sci Adv 2021;7:eabg3092. [DOI:10.1126/sciadv.abg3092]

57. Yu Y, Nassar J, Xu C, Min J, Yang Y, Dai A, et al. Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine interfaces. Sci Robot 2020;5:eaaz7946. [DOI:10.1126/scirobotics.aaz7946]

58. Tucker A, Wang Z, Rotalinti Y, Myles P. Generating high-fidelity synthetic patient data for assessing machine learning healthcare software. NPJ Digit Med 2020;3:1-13. [DOI:10.1038/s41746-020-00353-9]

59. Farrokhi M, Moeini A, Taheri F, Farrokhi M, Mostafavi M, Khodaei Ardakan A, et al, Eds. Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond. USA: Kindle; 2023.

60. Farrokhi M, Taheri F, Moeini A, Farrokhi M, Jafar Khouzani P, Ghadirzadeh E, et al, Eds. Artificial Intelligence for Drug Development, Personalized Prescriptions, and Adverse Event Prediction. USA: Kindle; 2024. P.1-180.

61. Ghanbari M, Mohebi S, Arabzadeh S. The use of artificial intelligence in the diagnosis of lung diseases. Nafas 2024:11.