Artificial intelligence for medicine: Progress, challenges, and perspectives

Tao Huang; Huiyu Xu; Haitao Wang; Haofan Huang; Yongjun Xu; Baohua Li; Shenda Hong; Guoshuang Feng; Shuyi Kui; Guangjian Liu; Dehua Jiang; Zhi-Cheng Li; Ye Li; Congcong Ma; Chunyan Su; Wei Wang; Rong Li; Puxiang Lai; Jie Qiao

doi:10.59717/j.xinn-med.2023.100030

The Innovation Medicine > 2023 Vol. 1 > No. 2 > 100030

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Artificial intelligence for medicine: Progress, challenges, and perspectives

1.
Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2.
Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
3.
Thoracic Surgery Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, United States
4.
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
5.
Photonics Research Institute, The Hong Kong Polytechnic University, Hong Kong SAR, China
6.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
7.
Nursing Department, Peking University Third Hospital, Beijing 100191, China
8.
National Institute of Health Data Science, Peking University, Beijing 100191, China
9.
Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
10.
Big Data Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China
11.
Lingnan College, Sun Yat-Sen University, Guangzhou 510275, China
12.
Shenzhen Dymind Biotechnology Co., Ltd., Shenzhen 518106, China
13.
Guangzhou Kangrun Biotech Co., Ltd., Guangzhou 511458, China
14.
Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
15.
Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen 518045, China
16.
Department of Thoracic Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
17.
These authors contributed equally

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Corresponding authors: huangtao@sibs.ac.cn (T.H.); puxiang.lai@polyu.edu.hk (P.L.); jie.qiao@263.net (J.Q.)
Received Date: 09 September 2023

Accepted Date: 14 September 2023

Published Online: 15 September 2023

The Innovation Medicine 1(2), https://doi.org/10.59717/j.xinn-med.2023.100030 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Data, computing power, and algorithm drive artificial intelligence.
  
  Artificial intelligence has changed the practice of medicine.
  
  Artificial intelligence is improving the quality of life.

ABSTRACT

ABSTRACT

Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients’ privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.

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REFERENCES

[1]	Schatsky, D., Muraskin, C., and Gurumurthy, R. (2014). Demystifying artificial intelligence: What business leaders need to know about cognitive technologies (Deloitte University Press). View in Article Google Scholar
[2]	Castellanos, S. (2018). What exactly is artificial intelligence? The Wall Street Journal, https://www.wsj.com/articles/what-exactly-is-artificial-intelligence-1544120887. View in Article Google Scholar
[3]	Koski, E., and Murphy, J. (2021). AI in Healthcare. Studies in health technology and informatics 284 : 295-299. DOI: 10.3233/shti210726. View in Article Google Scholar
[4]	Frost & Sullivan (2018). Artificial intelligence in healthcare takes precision medicine to the next level. PR Newswire, https://www.prnewswire.com/news-releases/artificial-intelligence-in-healthcare-takes-precision-medicine-to-the-next-level-300712098.html. View in Article Google Scholar
[5]	Ryan, S.A. (1985). An expert system for nursing practice. Clinical decision support. Comput. Nurs. 3: 77−84. View in Article Google Scholar
[6]	Gao, J., Ye, X., Li, Y., et al. (2022). Application of artificial intelligence in clinical nursing: A scope review. Chinese Evidence-based Nursing 8 : 2996-3006. DOI: 10.12102/j.issn.2095-8668.2022.22.002. View in Article Google Scholar
[7]	von Gerich, H., Moen, H., Block, L.J., et al. (2022). Artificial Intelligence-based technologies in nursing: A scoping literature review of the evidence. Int. J. Nurs. Stud. 127 : 104153. DOI: 10.1016/j.ijnurstu.2021.104153. View in Article Google Scholar
[8]	Seibert, K., Domhoff, D., Bruch, D., et al. (2021). Application scenarios for artificial intelligence in nursing care: Rapid review. J. Med. Internet. Res. 23 : e26522. DOI: 10.2196/26522. View in Article Google Scholar
[9]	Reddy, S., Allan, S., Coghlan, S., et al. (2020). A governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 27 : 491-497. DOI: 10.1093/jamia/ocz192. View in Article Google Scholar
[10]	Chiu, I.M., Cheng, C.Y., Chang, P.K., et al. (2023). Utilization of personalized machine-learning to screen for dysglycemia from ambulatory ECG, toward noninvasive blood glucose monitoring. Biosensors (Basel) 13 : ARTN 23. DOI: 10.3390/bios13010023. View in Article Google Scholar
[11]	Chaikijurajai, T., Laffin, L.J., and Tang, W.H.W. (2020). Artificial intelligence and hypertension: Recent advances and future outlook. Am. J. Hypertens. 33 : 967-974. DOI: 10.1093/ajh/hpaa102. View in Article Google Scholar
[12]	Das, S.K., Miki, A.J., Blanchard, C.M., et al. (2022). Perspective: Opportunities and challenges of technology tools in dietary and activity assessment: Bridging stakeholder viewpoints. Adv. Nutr. 13 : 1-15. DOI: 10.1093/advances/nmab103. View in Article Google Scholar
[13]	Schneider, C., Hanakam, F., Wiewelhove, T., et al. (2018). Heart rate monitoring in team sports - A conceptual framework for contextualizing heart rate measures for training and recovery prescription. Front. Physiol. 9 : ARTN 639. DOI: 10.3389/fphys.2018.00639. View in Article Google Scholar
[14]	Bandyopadhyay, A., and Goldstein, C. (2023). Clinical applications of artificial intelligence in sleep medicine: A sleep clinician's perspective. Sleep Breath. 27 : 39-55. DOI: 10.1007/s11325-022-02592-4. View in Article Google Scholar
[15]	Iliadou, E., Su, Q.Q., Kikidis, D., et al. (2022). Profiling hearing aid users through big data explainable artificial intelligence techniques. Front. Neurol. 13 : ARTN 933940. DOI: 10.3389/fneur.2022.933940. View in Article Google Scholar
[16]	Bernauer, S.A., Zitzmann, N.U., and Joda, T. (2021). The use and performance of artificial intelligence in prosthodontics: A systematic review. Sensors (Basel) 21 : ARTN 6628. DOI: 10.3390/s21196628. View in Article Google Scholar
[17]	Nahavandi, D., Alizadehsani, R., Khosravi, A., et al. (2022). Application of artificial intelligence in wearable devices: Opportunities and challenges. Comput. Meth. Prog. Bio. 213 : ARTN 106541. DOI: 10.1016/j.cmpb.2021.106541. View in Article Google Scholar
[18]	Han, Y., Xu, H., Feng, G., et al. (2022). An online tool for predicting ovarian reserve based on AMH level and age: A retrospective cohort study. Front. Endocrinol. (Lausanne) 13 : 946123. DOI: 10.3389/fendo.2022.946123. View in Article Google Scholar
[19]	Xu, H., Shi, L., Feng, G., et al. (2020). An ovarian reserve assessment model based on anti-mullerian hormone levels, follicle-stimulating hormone levels, and age: Retrospective cohort study. J. Med. Internet. Res. 22 : e19096. DOI: 10.2196/19096. View in Article Google Scholar
[20]	Xu, H., Feng, G., Wang, H., et al. (2020). A novel mathematical model of true ovarian reserve assessment based on predicted probability of poor ovarian response: A retrospective cohort study. J. Assist. Reprod. Genet. 37 : 963-972. DOI: 10.1007/s10815-020-01700-1. View in Article Google Scholar
[21]	Xu, H., Feng, G., Yang, R., et al. (2023). OvaRePred: Online tool for predicting the age of fertility milestones. The Innovation 4 : 100490. DOI: 10.1016/j.xinn.2023.100490. View in Article Google Scholar
[22]	Topping, M. (2002). An overview of the development of Handy 1, a rehabilitation robot to assist the severely disabled. J. Intell. Robot. Syst. 34 : 253-263. DOI: 10.1023/A:1016355418817. View in Article Google Scholar
[23]	Davies, N. (2016). Can robots handle your healthcare? Engineering & Technology 11 : 58-61. DOI: 10.1049/et.2016.0907. View in Article Google Scholar
[24]	Chang, D., Chang, D., and Pourhomayoun, M. (2019). Risk prediction of critical vital signs for ICU patients using recurrent neural network. 2019 International Conference on Computational Science and Computational Intelligence (CSCI) 2019 : 1003-1006. DOI: 10.1109/CSCI49370.2019.00191. View in Article Google Scholar
[25]	Stevens, N., Giannareas, A.R., Kern, V., et al. (2012). Smart alarms: Multivariate medical alarm integration for post CABG surgery patients. IHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. DOI:10.1145/2110363.2110423. View in Article Google Scholar
[26]	Sparks, R.S., and Okugami, C. (2016). Tele-health monitoring of patient wellness. J. Intell. Syst. 25 : 515-528. DOI: 10.1515/jisys-2014-0175. View in Article Google Scholar
[27]	Nauta, J., Mahieu, C., Michiels, C., et al. (2019). Pro-active positioning of a social robot intervening upon behavioral disturbances of persons with dementia in a smart nursing home. Cogn. Syst. Res. 57 : 160-174. DOI: 10.1016/j.cogsys.2019.03.002. View in Article Google Scholar
[28]	Barrera, A., Gee, C., Wood, A., et al. (2020). Introducing artificial intelligence in acute psychiatric inpatient care: Qualitative study of its use to conduct nursing observations. BMJ Mental Health 23 : 34-38. DOI: 10.1136/ebmental-2019-300136. View in Article Google Scholar
[29]	Lin, J.W., Chen, W., Shen, C.P., et al. (2018). Visualization and sonification of long-term epilepsy electroencephalogram monitoring. J. Med. Biol. Eng. 38 : 943-952. DOI: 10.1007/s40846-017-0358-6. View in Article Google Scholar
[30]	Yokota, S., Endo, M., and Ohe, K. (2017). Establishing a classification system for high fall-risk among inpatients using support vector machines. CIN Comput. Inform. Nurs. 35 : 408. DOI: 10.1097/cin.0000000000000332. View in Article Google Scholar
[31]	Nakatani, H., Nakao, M., Uchiyama, H., et al. (2020). Predicting inpatient falls using natural language processing of nursing records obtained from Japanese electronic medical records: Case-control study. JMIR Med. Inform. 8 : e16970. DOI: 10.2196/16970. View in Article Google Scholar
[32]	Lee, S.K., Ahn, J., Shin, J.H., et al. (2020). Application of machine learning methods in nursing home research. Int. J. Environ. Res. Public Health 17 : 6234. DOI: 10.3390/ijerph17176234. View in Article Google Scholar
[33]	Bauer, P., Kramer, J.B., Rush, B., et al. (2017). Modeling bed exit likelihood in a camera-based automated video monitoring application. IEEE International Conference on Electro Information Technology. 2017 : 056-061. DOI: 10.1109/EIT.2017.8053330. View in Article Google Scholar
[34]	Ladios-Martín, M., Fernández-de-Maya, J., Ballesta-López, F.J., et al. (2020). Predictive modeling of pressure injury risk in patients admitted to an intensive care unit. Am. J. Crit. Care 29 : e70-e80. DOI: 10.4037/ajcc2020237. View in Article Google Scholar
[35]	Kim, H., Choi, J., Thompson, S., et al. (2010). Automating pressure ulcer risk assessment using documented patient data. Int. J. Med. Inform. 79 : 840-848. DOI: 10.1016/j.ijmedinf.2010.08.005. View in Article Google Scholar
[36]	Cho, I., Park, I., Kim, E., et al. (2013). Using EHR data to predict hospital-acquired pressure ulcers: A prospective study of a Bayesian Network model. Int. J. Med. Inform. 82 : 1059-1067. DOI: 10.1016/j.ijmedinf.2013.06.012. View in Article Google Scholar
[37]	Hu, Y.H., Lee, Y.L., Kang, M.F., et al. (2020). Constructing inpatient pressure injury prediction models using machine learning techniques. Comput. Inform. Nurs. 38 : 415-423. DOI: 10.1097/CIN.0000000000000604. View in Article Google Scholar
[38]	Lotter, W., Diab, A.R., Haslam, B., et al. (2021). Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat. Med. 27 : 244-249. DOI: 10.1038/s41591-020-01174-9. View in Article Google Scholar
[39]	Esteva, A., Kuprel, B., Novoa, R.A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 : 115-118. DOI: 10.1038/nature21056. View in Article Google Scholar
[40]	Van Calster, B., Timmerman, S., Geysels, A., et al. (2022). A deep-learning-enabled diagnosis of ovarian cancer. Lancet Digit. Health 4 : e630. DOI: 10.1016/S2589-7500(22)00130-3. View in Article Google Scholar
[41]	Harmon, S.A., Sanford, T.H., Xu, S., et al. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat. Commun. 11 : 4080. DOI: 10.1038/s41467-020-17971-2. View in Article Google Scholar
[42]	Lu, M.Y., Chen, T.Y., Williamson, D.F.K., et al. (2021). AI-based pathology predicts origins for cancers of unknown primary. Nature 594 : 106-110. DOI: 10.1038/s41586-021-03512-4. View in Article Google Scholar
[43]	Yu, K.H., Zhang, C., Berry, G.J., et al. (2016). Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7 : 12474. DOI: 10.1038/ncomms12474. View in Article Google Scholar
[44]	Poirion, O.B., Jing, Z., Chaudhary, K., et al. (2021). DeepProg: An ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome. Med. 13 : 112. DOI: 10.1186/s13073-021-00930-x. View in Article Google Scholar
[45]	Kuenzi, B.M., Park, J., Fong, S.H., et al. (2020). Predicting drug response and synergy using a deep learning model of human cancer cells. Cancer Cell 38 : 672-684 e676. DOI: 10.1016/j.ccell.2020.09.014. View in Article Google Scholar
[46]	Xu, H., Feng, G., Han, Y., et al. (2023). POvaStim: An online tool for directing individualized FSH doses in ovarian stimulation. The Innovation 4 : 100401. DOI: 10.1016/j.xinn.2023.100401. View in Article Google Scholar
[47]	Hamm, C.A., Baumgartner, G.L., Biessmann, F., et al. (2023). Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology 307 : e222276. DOI: 10.1148/radiol.222276. View in Article Google Scholar
[48]	Wang, X., Chen, Y., Gao, Y., et al. (2021). Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning. Nat. Commun. 12 : 1637. DOI: 10.1038/s41467-021-21674-7. View in Article Google Scholar
[49]	Kleppe, A., Skrede, O.J., De Raedt, S., et al. (2021). Designing deep learning studies in cancer diagnostics. Nat. Rev. Cancer 21 : 199-211. DOI: 10.1038/s41568-020-00327-9. View in Article Google Scholar
[50]	Kermany, D.S., Goldbaum, M., Cai, W., et al. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172 : 1122-1131 e1129. DOI: 10.1016/j.cell.2018.02.010. View in Article Google Scholar
[51]	Xu, Y., Hosny, A., Zeleznik, R., et al. (2019). Deep learning predicts lung cancer treatment response from serial medical imaging. Clin. Cancer Res. 25 : 3266-3275. DOI: 10.1158/1078-0432.CCR-18-2495. View in Article Google Scholar
[52]	Murphy, K., Habib, S.S., Zaidi, S.M.A., et al. (2020). Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system. Sci. Rep. 10 : 5492. DOI: 10.1038/s41598-020-62148-y. View in Article Google Scholar
[53]	Kim, J.R., Shim, W.H., Yoon, H.M., et al. (2017). Computerized bone age estimation using deep learning based program: Evaluation of the accuracy and efficiency. AJR Am. J. Roentgenol. 209 : 1374-1380. DOI: 10.2214/ajr.17.18224. View in Article Google Scholar
[54]	Li, X., Pan, J., Zhou, H., et al. (2020). A multi-centre study for standardization of antinuclear antibody indirect immunofluorescence screening with automated system. J. Immunol. Methods 477 : 112701. DOI: 10.1016/j.jim.2019.112701. View in Article Google Scholar
[55]	Fraser, K.C., Meltzer, J.A., and Rudzicz, F. (2016). Linguistic features identify Alzheimer's Disease in narrative speech. J. Alzheimers Dis. 49 : 407-422. DOI: 10.3233/JAD-150520. View in Article Google Scholar
[56]	Diamanti-Kandarakis, E., Kouli, C.R., Bergiele, A.T., et al. (1999). A survey of the polycystic ovary syndrome in the Greek island of Lesbos: hormonal and metabolic profile. J. Clin. Endocrinol. Metab. 84 : 4006-4011. DOI: 10.1210/jcem.84.11.6148. View in Article Google Scholar
[57]	Azziz, R., Woods, K.S., Reyna, R., et al. (2004). The prevalence and features of the polycystic ovary syndrome in an unselected population. J. Clin. Endocrinol. Metab. 89 : 2745-2749. DOI: 10.1210/jc.2003-032046. View in Article Google Scholar
[58]	Norman, R.J., Dewailly, D., Legro, R.S., et al. (2007). Polycystic ovary syndrome. Lancet 370 : 685-697. DOI: 10.1016/S0140-6736(07)61345-2. View in Article Google Scholar
[59]	Michelmore, K.F., Balen, A.H., Dunger, D.B., et al. (1999). Polycystic ovaries and associated clinical and biochemical features in young women. Clin. Endocrinol. (Oxf) 51 : 779-786. DOI: 10.1046/j.1365-2265.1999.00886.x. View in Article Google Scholar
[60]	Lauritsen, M.P., Bentzen, J.G., Pinborg, A., et al. (2014). The prevalence of polycystic ovary syndrome in a normal population according to the Rotterdam criteria versus revised criteria including anti-Mullerian hormone. Hum. Reprod. 29 : 791-801. DOI: 10.1093/humrep/det469. View in Article Google Scholar
[61]	Bozdag, G., Mumusoglu, S., Zengin, D., et al. (2016). The prevalence and phenotypic features of polycystic ovary syndrome: A systematic review and meta-analysis. Hum. Reprod. 31 : 2841-2855. DOI: 10.1093/humrep/dew218. View in Article Google Scholar
[62]	Xu, H., Feng, G., Shi, L., et al. (2023). PCOSt: A non-invasive and cost-effective screening tool for polycystic ovary syndrome. The Innovation 4 : 100407. DOI: 10.1016/j.xinn.2023.100407. View in Article Google Scholar
[63]	Teede, H.J., Misso, M.L., Costello, M.F., et al. (2018). Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Clin. Endocrinol. (Oxf) 89 : 251-268. DOI: 10.1111/cen.13795. View in Article Google Scholar
[64]	Yi, J.F., Zhang, H., Mao, J.X., et al. (2022). Review on the COVID-19 pandemic prevention and control system based on AI. Eng. Appl. Artif. Intel. 114 : ARTN 105184. DOI: 10.1016/j.engappai.2022.105184. View in Article Google Scholar
[65]	Rasheed, J., Jamil, A., Hameed, A.A., et al. (2021). COVID-19 in the age of artificial intelligence: A comprehensive review. Interdiscip. Sci. 13 : 153-175. DOI: 10.1007/s12539-021-00431-w. View in Article Google Scholar
[66]	Li, T., Huang, T., Guo, C., et al. (2021). Genomic variation, origin tracing and vaccine development of SARS-CoV-2: A systematic review. The Innovation 2 : 100116. DOI: 10.1016/j.xinn.2021.100116. View in Article Google Scholar
[67]	Ren, H., Ling, Y., Cao, R., et al. (2023). Early warning of emerging infectious diseases based on multimodal data. Biosaf. Health 5 : 193-203. DOI: 10.1016/j.bsheal.2023.05.006. View in Article Google Scholar
[68]	Fisher, S., and Rosella, L.C. (2022). Priorities for successful use of artificial intelligence by public health organizations: A literature review. BMC Public Health 22 : 2146. DOI: 10.1186/s12889-022-14422-z. View in Article Google Scholar
[69]	Jungwirth, D., and Haluza, D. (2023). Artificial intelligence and public health: An exploratory study. Int. J. Environ. Res. Public Health 20 : 4541. DOI: 10.3390/ijerph20054541. View in Article Google Scholar
[70]	Xu, Y., Liu, X., Cao, X., et al. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 : 100179. DOI: 10.1016/j.xinn.2021.100179. View in Article Google Scholar
[71]	de Mello, B.H., Rigo, S.J., da Costa, C.A., et al. (2022). Semantic interoperability in health records standards: A systematic literature review. Health Technol. 12: 255−272. DOI: 10.1007/s12553-022-00639-w. View in Article CrossRef Google Scholar
[72]	Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3: 1−9. DOI: 10.1038/sdata.2016.18. View in Article CrossRef Google Scholar
[73]	Ranchal, R., Bastide, P., Wang, X., et al. (2020). Disrupting healthcare silos: Addressing data volume, velocity and variety with a cloud-native healthcare data ingestion service. IEEE J. Biomed. Health Inform. 24: 3182−3188. DOI: 10.1109/JBHI.2020.3001518. View in Article CrossRef Google Scholar
[74]	Sambasivan, N., Kapania, S., Highfill, H., et al. (2021). “Everyone wants to do the model work, not the data work”: Data cascades in high-stakes AI. CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems 39 : 1-15. DOI: 10.1145/3411764.3445518. View in Article Google Scholar
[75]	Prasser, F., Kohlbacher, O., Mansmann, U., et al. (2018). Data integration for future medicine (DIFUTURE). Methods Inf. Med. 57 : e57-e65. DOI: 10.3414/me17-02-0022. View in Article Google Scholar
[76]	Frank, R., and FDG-PET/CT Working Group. (2008). Quantitative imaging biomarkers alliance FDG-PET/CT Working Group report. Mol. Imaging Biol. 10 : 305. DOI: 10.1007/s11307-008-0167-y. View in Article Google Scholar
[77]	Zwanenburg, A., Vallières, M., Abdalah, M.A., et al. (2020). The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295 : 328-338. DOI: 10.1148/radiol.2020191145. View in Article Google Scholar
[78]	Mullard, A. (2022). The UK Biobank at 20. Nat. Rev. Drug Discov. 21 : 628-629. DOI: 10.1038/d41573-022-00137-8. View in Article Google Scholar
[79]	Clark, K., Vendt, B., Smith, K., et al. (2013). The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging 26 : 1045-1057. DOI: 10.1007/s10278-013-9622-7. View in Article Google Scholar
[80]	Tomczak, K., Czerwińska, P., and Wiznerowicz, M. (2015). The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemp. Oncol. (Pozn) 19 : A68-77. DOI: 10.5114/wo.2014.47136. View in Article Google Scholar
[81]	Liang, W., Tadesse, G.A., Ho, D., et al. (2022). Advances, challenges and opportunities in creating data for trustworthy AI. Nat. Mach. Intell. 4: 669−677. DOI: 10.1038/s42256-022-00516-1. View in Article CrossRef Google Scholar
[82]	Lipkova, J., Chen, R.J., Chen, B., et al. (2022). Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40 : 1095-1110. DOI: 10.1016/j.ccell.2022.09.012. View in Article Google Scholar
[83]	Acosta, J.N., Falcone, G.J., Rajpurkar, P., et al. (2022). Multimodal biomedical AI. Nat. Med. 28 : 1773-1784. DOI: 10.1038/s41591-022-01981-2. View in Article Google Scholar
[84]	Huang, S.-C., Pareek, A., Seyyedi, S., et al. (2020). Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines. NPJ Digit. Med. 3: 136. DOI: 10.1038/s41746-020-00341-z. View in Article CrossRef Google Scholar
[85]	Qian, S., and Wang, C. (2023). COM: Contrastive Masked-attention model for incomplete multimodal learning. Neural Netw. 162: 443−455. DOI: 10.1016/j.neunet.2023.03.003. View in Article CrossRef Google Scholar
[86]	Sun, Q., Chen, Y., Liang, C., et al. (2021). Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma. Radiology 301 : 654-663. DOI: 10.1148/radiol.2021203281. View in Article Google Scholar
[87]	Yan, J., Zhang, S., Li, K.K., et al. (2020). Incremental prognostic value and underlying biological pathways of radiomics patterns in medulloblastoma. EBioMedicine 61 : 103093. DOI: 10.1016/j.ebiom.2020.103093. View in Article Google Scholar
[88]	Yan, J., Zhao, Y., Chen, Y., et al. (2021). Deep learning features from diffusion tensor imaging improve glioma stratification and identify risk groups with distinct molecular pathway activities. EBioMedicine 72 : 103583. DOI: 10.1016/j.ebiom.2021.103583. View in Article Google Scholar
[89]	Boehm, K.M., Khosravi, P., Vanguri, R., et al. (2022). Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 22 : 114-126. DOI: 10.1038/s41568-021-00408-3. View in Article Google Scholar
[90]	Poon, A.I.F., and Sung, J.J.Y. (2021). Opening the black box of AI-Medicine. J. Gastroenterol. Hepatol. 36 : 581-584. DOI: 10.1111/jgh.15384. View in Article Google Scholar
[91]	Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy (Basel) 23 : 18. DOI : 10.3390/e23010018. View in Article Google Scholar
[92]	Sidak, D., Schwarzerová, J., Weckwerth, W., et al. (2022). Interpretable machine learning methods for predictions in systems biology from omics data. Front. Mol. Biosci. 9 : 926623. DOI: 10.3389/fmolb.2022.926623. View in Article Google Scholar
[93]	Zhang, Z., Beck, M.W., Winkler, D.A., et al. (2018). Opening the black box of neural networks: Methods for interpreting neural network models in clinical applications. Ann. Transl. Med. 6 : 216. DOI: 10.21037/atm.2018.05.32. View in Article Google Scholar
[94]	Bærøe, K., Miyata-Sturm, A., and Henden, E. (2020). How to achieve trustworthy artificial intelligence for health. Bull. World Health Organ. 98 : 257-262. DOI: 10.2471/blt.19.237289. View in Article Google Scholar
[95]	Rueda, J., Rodríguez, J.D., Jounou, I.P., et al. (2022). "Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations. AI Soc. 21 : 1-12. DOI: 10.1007/s00146-022-01614-9. View in Article Google Scholar
[96]	Zanca, F., Brusasco, C., Pesapane, F., et al. (2022). Regulatory aspects of the use of artificial intelligence medical software. Semin. Radiat. Oncol. 32 : 432-441. DOI: 10.1016/j.semradonc.2022.06.012. View in Article Google Scholar
[97]	Zhang, J., and Zhang, Z.M. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Med. Inform. Decis. Mak. 23 : 7. DOI: 10.1186/s12911-023-02103-9. View in Article Google Scholar
[98]	Obermeyer, Z., and Emanuel, E.J. (2016). Predicting the future-big data, machine learning, and clinical medicine. N. Engl. J. Med. 375: 1216. DOI: 10.1056/NEJMp1606181. View in Article CrossRef Google Scholar
[99]	Wang, F., Ma, L., Moulton, G., et al. (2022). Clinician data scientists-preparing for the future of medicine in the digital world. Health Data Sci. 2022 : 9832564. DOI: 10.34133/2022/9832564. View in Article Google Scholar
[100]	Acosta, J.N., Falcone, G.J., Rajpurkar, P., et al. (2022). Multimodal biomedical AI. Nat. Med. 28: 1773−1784. DOI: 10.1038/s41591-022-01981-2. View in Article CrossRef Google Scholar
[101]	Van Dis, E.A., Bollen, J., Zuidema, W., et al. (2023). ChatGPT: Five priorities for research. Nature 614: 224−226. DOI: 10.1038/d41586-023-00288-7. View in Article CrossRef Google Scholar
[102]	Moor, M., Banerjee, O., Abad, Z.S.H., et al. (2023). Foundation models for generalist medical artificial intelligence. Nature 616: 259−265. DOI: 10.1038/s41586-023-05881-4. View in Article CrossRef Google Scholar
[103]	Brown, T., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems 33: 1877−1901. DOI: 10.5555/3495724.3495883. View in Article CrossRef Google Scholar
[104]	Kirillov, A., Mintun, E., Ravi, N., et al. (2023). Segment anything. arXiv preprint arXiv:2304.02643. DOI: 10.48550/arXiv.2304.02643. View in Article Google Scholar
[105]	Xu, J., Xiao, Y., Wang, W.H., et al. (2022). Algorithmic fairness in computational medicine. EBioMedicine 84: 104250. DOI: 10.1016/j.ebiom.2022.104250. View in Article CrossRef Google Scholar
[106]	LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521 : 436-444. DOI: 10.1038/nature14539. View in Article Google Scholar
[107]	Zhang, C., Xie, Y., Bai, H., et al. (2021). A survey on federated learning. Knowl. Based Syst. 216: 106775. DOI: 10.1016/j.knosys.2021.106775. View in Article CrossRef Google Scholar
[108]	Gong, M., Xie, Y., Pan, K., et al. (2020). A survey on differentially private machine learning. IEEE Computational Intelligence Magazine 15 : 49-64. DOI: 10.1109/MCI.2020.2976185. View in Article Google Scholar
[109]	Xu, J., Glicksberg, B.S., Su, C., et al. (2021). Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5 : 1-19. DOI: 10.1007/s41666-020-00082-4. View in Article Google Scholar
[110]	Li, L., Fan, Y., Tse, M., et al. (2020). A review of applications in federated learning. Comput. Ind. Eng. 149: 106854. DOI: 10.1016/j.cie.2020.106854. View in Article CrossRef Google Scholar
[111]	Rieke, N., Hancox, J., Li, W., et al. (2020). The future of digital health with federated learning. NPJ Digit. Med. 3 : 119. DOI: 10.1038/s41746-020-00323-1. View in Article Google Scholar
[112]	Li, T., Sahu, A.K., Talwalkar, A., et al. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Proc. Mag. 37 : 50-60. DOI: 10.1109/MSP.2020.2975749. View in Article Google Scholar
[113]	Yan, Y., Hong, S., Zhang, W., et al. (2022). Artificial intelligence in skin diseases: Fulfilling its potentials to meet the real needs in dermatology practice. Health Data Sci. 2022 : 9791467. DOI: 10.34133/2022/9791467. View in Article Google Scholar
[114]	Hong, H., and Hong, S. (2023). simpleNomo: A python package of making nomograms for visualizable calculation of logistic regression models. Health Data Sci. 3 : 0023. DOI: 10.34133/hds.0023. View in Article Google Scholar

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Huang T., Xu H., Wang H., et al., (2023). Artificial intelligence for medicine: Progress, challenges, and perspectives. The Innovation Medicine 1(2), 100030. https://doi.org/10.59717/j.xinn-med.2023.100030

Huang T., Xu H., Wang H., et al., (2023). Artificial intelligence for medicine: Progress, challenges, and perspectives. The Innovation Medicine 1(2), 100030. https://doi.org/10.59717/j.xinn-med.2023.100030

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