Artificial Intelligence in Cancer: A SWOT Analysis

Year 2024, Volume: 8 Issue: 1, 107 - 137, 31.12.2024

Gülşah Torkay , Nouran Fadlallah , Ahmet Karagöz , Mesut Canlı , Ezgi Saydam , Ayşenur Mete , Furkan Kızılışık , Hakan Darici , Yusuf Yeşil

https://doi.org/10.61969/jai.1469589

Abstract

Cancer, a collection of maladies that has undergone extensive examination over centuries, remains a formidable challenge. Despite the array of available pharmacological and therapeutic interventions, the intricate molecular dynamics and heterogeneity of cancer continue to challenge the scientific community. Artificial Intelligence (AI) emerges as a promising avenue, offering the potential for expedited, precise diagnostics devoid of human expertise. Additionally, AI facilitates the tailoring of patient-specific therapeutic strategies targeting various facets of cancer, spanning macroscopic to microscopic levels. Nonetheless, it is imperative to scrutinize the potential benefits and limitations of AI technologies in this context. This review undertakes a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer. An extensive compilation of AI applications encompasses predictive modeling, diagnostic capabilities, prognostic assessments, and personalized therapeutic modalities, spanning genomic analyses to individualized treatment regimens. The synthesis of evidence suggests that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.

Keywords

Artificial intelligence, cancer, deep learning, machine learning, precision oncology, SWOT analysis

Supporting Institution

Scientific and Technological Research Council of Türkiye (TUBITAK)

Project Number

Yok

Thanks

The authors would like to acknowledge the financial support from the Scientific and Technological Research Council of Türkiye (TUBITAK) 2210-A General Domestic Graduate Scholarship Program and 2211-E National PhD Scholarship Program for Former Undergraduate and MSc/MA Scholars (App No: 1649B022101483 and App No: 1649B032304943).

References

• Abdelhalim, I. S. A., Mohamed, M. F., & Mahdy, Y. B. (2021). Data augmentation for skin lesion using self-attention based progressive generative adversarial network. Expert Systems With Applications, 165, 113922.
• Amisha, P. M., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of family medicine and primary care, 8(7), 2328.
• Azuaje, F. (2019). Artificial intelligence for precision oncology: beyond patient stratification. NPJ precision oncology, 3(1), 1-5.
• Baker M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533(7604), 452–454. https://doi.org/10.1038/533452a
• Baniasadi, T., Ayyoubzadeh, S. M., & Mohammadzadeh, N. (2020). Challenges and practical considerations in applying virtual reality in medical education and treatment. Oman Medical Journal, 35(3), e125.
• Bender, A., & Cortés-Ciriano, I. (2021). 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 discovery today, 26(2), 511-524.
• Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nature reviews Clinical oncology, 16(11), 703-715.
• Bhavya, S., & Pillai, A. S. (2019, December). Prediction models in healthcare using deep learning. In International Conference on Soft Computing and Pattern Recognition (pp. 195-204). Springer, Cham.
• Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, et al. (2018) Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLOS Medicine 15(11): e1002699. https://doi.org/10.1371/journal.pmed.1002699
• Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25-60). Academic Press.
• Buch, V. H., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: current trends and future possibilities. British Journal of General Practice, 68(668), 143-144.
• Catto, J. W., Abbod, M. F., Wild, P. J., Linkens, D. A., Pilarsky, C., Rehman, I., ... & Hamdy, F. C. (2010). The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression. European urology, 57(3), 398-406.
• Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018). Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clinical cancer research: an official journal of the American Association for Cancer Research, 24(6), 1248–1259. https://doi.org/10.1158/1078-0432.CCR-17-0853
• Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., Hu, S., Wang, Y., Hu, X., Zheng, B., Zhang, K., Wu, H., Dong, Z., Xu, Y., Zhu, Y., Chen, X., … Yu, H. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Scientific reports, 10(1), 19196. https://doi.org/10.1038/s41598-020-76282-0
• Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association: JAMIA, 24(2), 361–370. https://doi.org/10.1093/jamia/ocw112
• Coudray, N., Ocampo, P.S., Sakellaropoulos, T. et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med 24, 1559–1567 (2018). https://doi.org/10.1038/s41591-018-0177-5
• Cutillo, C. M., Sharma, K. R., Foschini, L., Kundu, S., Mackintosh, M., & Mandl, K. D. (2020). Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency. NPJ digital medicine, 3(1), 1-5.
• Daemen, A., Gevaert, O., De Bie, T., Debucquoy, A., Machiels, J. P., De Moor, B., & Haustermans, K. (2008). Integrating microarray and proteomics data to predict the response on cetuximab in patients with rectal cancer. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 166–177.
• Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94.
• Demner-Fushman, D., Chapman, W. W., & McDonald, C. J. (2009). What can natural language processing do for clinical decision support?. Journal of biomedical informatics, 42(5), 760-772.
• DeVita, V. T., Jr, & Chu, E. (2008). A history of cancer chemotherapy. Cancer research, 68(21), 8643–8653. https://doi.org/10.1158/0008-5472.CAN-07-6611
• Dlamini, Z., Francies, F. Z., Hull, R., & Marima, R. (2020). Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and Structural Biotechnology Journal.
• Eisner, R., Greiner, R., Tso, V., Wang, H., & Fedorak, R. N. (2013). A machine-learned predictor of colonic polyps based on urinary metabolomics. BioMed research international, 2013, 303982. https://doi.org/10.1155/2013/303982
• Essén, A., Scandurra, I., Gerrits, R., Humphrey, G., Johansen, M. A., Kierkegaard, P., ... & Ancker, J. S. (2018). Patient access to electronic health records: differences across ten countries. Health policy and technology, 7(1), 44-56.
• Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015). Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer, 136(5), E359–E386. https://doi.org/10.1002/ijc.29210
• Fleming, N. (2018). How artificial intelligence is changing drug discovery. Nature, 557(7706), S55-S55.
• Fountzilas, E., & Tsimberidou, A. M. (2018). Overview of precision oncology trials: challenges and opportunities. Expert review of clinical pharmacology, 11(8), 797–804. https://doi.org/10.1080/17512433.2018.1504677
• Gatt, A., Portet, F., Reiter, E., Hunter, J., Mahamood, S., Moncur, W., & Sripada, S. (2009). From data to text in the neonatal intensive care unit: Using NLG technology for decision support and information management. Ai Communications, 22(3), 153-186.
• Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5
• Gigerenzer, G., & Kurzenhaeuser, S. (2005). Fast and frugal heuristics in medical decision making. Science and medicine in dialogue: Thinking through particulars and universals, 3-15.
• Goldenberg, S. L., Nir, G., & Salcudean, S. E. (2019). A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology, 16(7), 391-403.
• Goyal, L., Hingmire, S., & Parikh, P. M. (2006). Newer Diagnostic Methods in Oncology. Medical journal, Armed Forces India, 62(2), 162–168. https://doi.org/10.1016/S0377-1237(06)80062-6
• Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Computers in biology and medicine, 127, 104065.
• Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial intelligence in health care: bibliometric analysis. Journal of Medical Internet Research, 22(7), e18228.
• Güvenç, E., Ersoy, M., & Çetin, G. (2023). BRAIN TUMOR SEGMENTATION ON FLAIR MR IMAGES WITH U-NET. Mugla Journal of Science and Technology, 9(1), 34-41.
• Hanahan, D. (2022). Hallmarks of Cancer: New Dimensions. Cancer Discovery, 12(1), 31-46.
• Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. cell, 100(1), 57-70.
• Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. cell, 144(5), 646-674.
• He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), 30-36.
• Hessler, G., & Baringhaus, K. H. (2018). Artificial Intelligence in Drug Design. Molecules (Basel, Switzerland), 23(10), 2520. https://doi.org/10.3390/molecules23102520
• Hong, S. N., Son, H. J., Choi, S. K., Chang, D. K., Kim, Y. H., Jung, S. H., & Rhee, P. L. (2017). A prediction model for advanced colorectal neoplasia in an asymptomatic screening population. PloS one, 12(8), e0181040.
• How the American Cancer Society Funds Research. https://www.cancer.org/research/how-american-cancer-society-research-funding-works.html#:~:text=In%202019%2C%20you%20helped%20us,vital%20patient%20services%20and%20programs Accessed 2 February 2022.
• Huang, S., Yang, J., Fong, S., & Zhao, Q. (2020). Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Letters, 471, 61-71.
• Hulsen, T., Jamuar, S. S., Moody, A. R., Karnes, J. H., Varga, O., Hedensted, S., ... & McKinney, E. F. (2019). From big data to precision medicine. Frontiers in medicine, 6, 34.
• Hutchinson, M. L., Antono, E., Gibbons, B. M., Paradiso, S., Ling, J., & Meredig, B. (2017). Overcoming data scarcity with transfer learning. arXiv preprint arXiv:1711.05099.
• Ilbay, K., Übeyli, E. D., Ilbay, G., & Budak, F. (2011). A new application of recurrent neural networks for EMG-based diagnosis of carpal tunnel syndrome. Recurrent Neural Networks for Temporal Data Processing, 37.
• Iqbal, T., & Ali, H. (2018). Generative adversarial network for medical images (MI-GAN). Journal of medical systems, 42(11), 1-11.
• Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked, 20, 100412.
• Jawandhiya, P. (2018). Hardware design for machine learning. Int. J. Artif. Intell. Appl, 9(1), 63-84.
• Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
• Jin, C., Chen, W., Cao, Y. et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 11, 5088 (2020). https://doi.org/10.1038/s41467-020-18685-1
• Karim, M. R., Beyan, O., Zappa, A., Costa, I. G., Rebholz-Schuhmann, D., Cochez, M., & Decker, S. (2021). Deep learning-based clustering approaches for bioinformatics. Briefings in Bioinformatics, 22(1), 393-415.
• Keam S, Gill S, Ebert MA, Nowak AK, Cook AM. Enhancing the efficacy of immunotherapy using radiotherapy. Clin Transl Immunology. 2020 Sep 10;9(9):e1169. doi: 10.1002/cti2.1169. PMID: 32994997; PMCID: PMC7507442.
• Khalifa, N. E. M., Taha, M. H. N., Ali, D. E., Slowik, A., & Hassanien, A. E. (2020). Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach. IEEE Access, 8, 22874-22883.
• Kheradpisheh, S. R., Ghodrati, M., Ganjtabesh, M., & Masquelier, T. (2016). Deep networks can resemble human feed-forward vision in invariant object recognition. Scientific reports, 6(1), 1-24.
• Kim, Y. G., Kim, S., Cho, C. E., Song, I. H., Lee, H. J., Ahn, S., ... & Kim, N. (2020). Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections. Scientific Reports, 10(1), 1-9.
• Ko, B. S., Wang, Y. F., Li, J. L., Li, C. C., Weng, P. F., Hsu, S. C., Hou, H. A., Huang, H. H., Yao, M., Lin, C. T., Liu, J. H., Tsai, C. H., Huang, T. C., Wu, S. J., Huang, S. Y., Chou, W. C., Tien, H. F., Lee, C. C., & Tang, J. L. (2018). Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome. EBioMedicine, 37, 91–100. https://doi.org/10.1016/j.ebiom.2018.10.042
• Komura, D., & Ishikawa, S. (2018). Machine learning methods for histopathological image analysis. Computational and structural biotechnology journal, 16, 34-42.
• Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2014). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8–17. https://doi.org/10.1016/j.csbj.2014.11.005
• Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and brain sciences, 40.
• Liang, H., Tsui, B. Y., Ni, H., Valentim, C. C., Baxter, S. L., Liu, G., ... & Xia, H. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature medicine, 25(3), 433-438.
• Lindsay, G. W. (2021). Convolutional neural networks as a model of the visual system: Past, present, and future. Journal of cognitive neuroscience, 33(10), 2017-2031.
• Liu, J., Cheng, Y., Wang, X. et al. Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering. Sci Rep 8, 8270 (2018). https://doi.org/10.1038/s41598-018-26666-0
• Liu, J., Zheng, S., Yu, J. K., Zhang, J. M., & Chen, Z. (2005). Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor. Journal of Zhejiang University. Science. B, 6(1), 4–10. https://doi.org/10.1631/jzus.2005.B0004
• Liu, W. N., Zhang, Y. Y., Bian, X. Q., Wang, L. J., Yang, Q., Zhang, X. D., & Huang, J. (2020). Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi journal of gastroenterology: official journal of the Saudi Gastroenterology Association, 26(1), 13–19. https://doi.org/10.4103/sjg.SJG_377_19
• Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., ... & Zhavoronkov, A. (2018). Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 9(5), 5665.
• Miller, R. A. (1994). Medical diagnostic decision support systems—past, present, and future: a threaded bibliography and brief commentary. Journal of the American Medical Informatics Association, 1(1), 8-27.
• Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(13): E2970–E2979.
• Nalliah, R. P. (2016). Clinical decision making–choosing between intuition, experience and scientific evidence. British dental journal, 221(12), 752-754.
• Namikawa, K., Hirasawa, T., Nakano, K., Ikenoyama, Y., Ishioka, M., Shiroma, S., Tokai, Y., Yoshimizu, S., Horiuchi, Y., Ishiyama, A., Yoshio, T., Tsuchida, T., Fujisaki, J., & Tada, T. (2020). Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers: comparison between the original and newly developed systems. Endoscopy, 52(12), 1077–1083. https://doi.org/10.1055/a-1194-8771
• O’Shea, K., & Nash, R. (2015). An Introduction to Convolutional Neural Networks. ArXiv, abs/1511.08458.
• Odusami, M., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2021). Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics, 11(6), 1071.
• Parsa, N. (2012). Environmental factors inducing human cancers. Iranian journal of public health, 41(11), 1.
• Patel, S. K., George, B., & Rai, V. (2020). Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Frontiers in Pharmacology, 11, 1177.
• Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., … Lungren, M. P. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11). https://doi.org/10.1371/journal.pmed.1002686
• Ranjbarzadeh, R., Kasgari, A. B., Ghoushchi, S. J., Anari, S., Naseri, M., & Bendechache, M. (2021). Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Scientific Reports, 11(1), 1-17.
• Reddy, E. M., & Bhaskar, P. (2018). Able Machine Learning Method for classifying Disease-Treatment Semantic Relations from Bio-Medical Sentences. vol, 5, 5.
• Renda, A. (2019). Artificial Intelligence. Ethics, governance and policy challenges. CEPS Centre for European Policy Studies.
• Richens, J.G., Lee, C.M. & Johri, S. Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun 11, 3923 (2020). https://doi.org/10.1038/s41467-020-17419-7
• Rigaki, M., & Garcia, S. (2020). A survey of privacy attacks in machine learning. ACM Computing Surveys.
• Ronneberger, O., Fischer, P., & Brox, T. (2015, October). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
• Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
• Saldanha, O. L., Quirke, P., West, N. P., James, J. A., Loughrey, M. B., Grabsch, H. I., ... & Kather, J. N. (2022). Swarm learning for decentralized artificial intelligence in cancer histopathology. Nature Medicine, 28(6), 1232-1239.
• Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1-21.
• Schork, N. J. (2019). Artificial intelligence and personalized medicine. In Precision Medicine in Cancer Therapy (pp. 265-283). Springer, Cham.
• Shafiei, S. B., Elsayed, A. S., Hussein, A. A., Iqbal, U., & Guru, K. A. (2020). Evaluating the Mental Workload During Robot-Assisted Surgery Utilizing Network Flexibility of Human Brain. IEEE Access, 8, 204012-204019.
• Shalf, J. (2020). The future of computing beyond Moore’s law. Philosophical Transactions of the Royal Society A, 378(2166), 20190061.
• Shvetsova, N., Bakker, B., Fedulova, I., Schulz, H., & Dylov, D. V. (2021). Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access, 9, 118571-118583.
• Snorovikhina, V., & Zaytsev, A. (2020, October). Unsupervised anomaly detection for discrete sequence healthcare data. In International Conference on Analysis of Images, Social Networks and Texts (pp. 391-403). Springer, Cham.
• Somashekhar, S. P., Sepúlveda, M. J., Puglielli, S., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., Rauthan, A., Arun Kumar, N., Patil, P., Rhee, K., & Ramya, Y. (2018). Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of oncology: official journal of the European Society for Medical Oncology, 29(2), 418–423. https://doi.org/10.1093/annonc/mdx781
• Srivastava, D., Srivastava, S. K., Khan, S. B., Singh, H. R., Maakar, S. K., Agarwal, A. K., ... & Albalawi, E. (2023). Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model. Diagnostics, 13(22), 3485.
• Su, Jilian & Liu, Yuanhui & Wang, Junmei. (2020). Ultrasound image assisted diagnosis of hydronephrosis based on CNN neural network. Journal of King Saud University - Science. 32. 10.1016/j.jksus.2020.04.005.
• Sun, Y., Zhu, S., Ma, K. et al. Identification of 12 cancer types through genome deep learning. Sci Rep 9, 17256 (2019). https://doi.org/10.1038/s41598-019-53989-3
• Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., La, V. P., Ho, M. T., Nghiem, K. P., Nguyen, H., Latkin, C. A., Tam, W., Cheung, N. M., Nguyen, H. T., Ho, C., & Ho, R. (2019). Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. Journal of clinical medicine, 8(3), 360. https://doi.org/10.3390/jcm8030360
• Turing, A. M., & Haugeland, J. (1950). Computing machinery and intelligence (pp. 29-56). Cambridge, MA: MIT Press.
• US Food and Drug Administration. (2020). Artificial intelligence and machine learning in software as a medical device. Content current as of January, 28, 2020. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device Accessed 3 February 2022.
• Wijnhoven, F. (2021). Organizational Learning for Intelligence Amplification Adoption: Lessons from a Clinical Decision Support System Adoption Project. Information Systems Frontiers, 1-14.
• Workman, P., Antolin, A. A., & Al-Lazikani, B. (2019). Transforming cancer drug discovery with Big Data and AI. Expert opinion on drug discovery, 14(11), 1089–1095. https://doi.org/10.1080/17460441.2019.1637414
• Yadav, S.S., Jadhav, S.M. Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 6, 113 (2019). https://doi.org/10.1186/s40537-019-0276-2
• Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., ... & You, Z. (2021). COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE transactions on big data, 7(1), 13-24.
• Yang, X. S. (Ed.). (2012). Artificial intelligence, evolutionary computing and metaheuristics: in the footsteps of Alan Turing (Vol. 427). Springer.
• Ye, Y., Wagner, M. M., Cooper, G. F., Ferraro, J. P., Su, H., Gesteland, P. H., ... & Tsui, F. (2017). A study of the transferability of influenza case detection systems between two large healthcare systems. PloS one, 12(4), e0174970.
• Ying, X. (2019, February). An overview of overfitting and its solutions. In Journal of Physics: Conference Series (Vol. 1168, No. 2, p. 022022). IOP Publishing.
• Zhang, D., Liu, X., Shao, M. et al. The value of artificial intelligence and imaging diagnosis in the fight against COVID-19. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01522-7
• Zhang, L., Tan, J., Han, D., & Zhu, H. (2017). From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug discovery today, 22(11), 1680–1685. https://doi.org/10.1016/j.drudis.2017.08.010
• Zhang, Z., Li, J., He, T., & Ding, J. (2020). Bioinformatics identified 17 immune genes as prognostic biomarkers for breast cancer: application study based on artificial intelligence algorithms. Frontiers in oncology, 10, 330.
• Zhao, Y., Pan, Z., Namburi, S., Pattison, A., Posner, A., Balachander, S., ... & George, J. (2020). CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence. EBioMedicine, 61, 103030.
• Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., Li, F., Luo, X., Li, Z., Chen, K., Zheng, M., & Jiang, H. (2018). Artificial intelligence in drug design. Science China. Life sciences, 61(10), 1191–1204. https://doi.org/10.1007/s11427-018-9342-2