AI-POWERED HEALTHCARE REVOLUTION: AN EXTENSIVE EXAMINATION OF INNOVATIVE METHODS IN CANCER TREATMENT
Keywords:
Clinical Decision Support, Predictive Modeling, Drug Discovery, Pathology Analysis, Clinical Trials, Population Health Management, Artificial Intelligence, Cancer Medicine, Precision Oncology, Diagnostic Imaging, Data Privacy, Regulatory Frameworks, Interoperability, Resource Constraints, and Future Outlook.Abstract
Abstract: This study examines the various ways that artificial intelligence (AI) is being used into the field of cancer medicine, with an emphasis on innovative techniques and advances in healthcare. The article, titled "AI Healthcare and Novel Approaches in the Field of Cancer Medicine," explores how AI is revolutionizing a number of fields, including population health management, clinical decision support, drug discovery, pathology analysis, diagnostic imaging, predictive modeling, and predictive modeling. The essay starts out by exploring the revolutionary role that artificial intelligence (AI) is playing in diagnostic imaging, where algorithms are demonstrating exceptional accuracy in identifying anomalies, especially in MRIs, CT scans, and mammograms. The tailoring of cancer treatments based on unique molecular profiles, bringing in a new age of targeted therapies, and minimizing side effects are the main themes that arise from precision oncology. AI-powered clinical decision support systems analyze a variety of patient data to improve the decision-making process for medical personnel. As a crucial component of cancer medicine, predictive modeling provides insights into disease development, therapeutic responses, survival prognostication, and the identification of high-risk patients. The study highlights how AI can improve clinical trials, speed up drug research and development, and change pathology and histology analysis to provide more precise cancer diagnosis.
References
Binsar, F., Kartono, R., Bandur, A., & Kosasih, W. (2022, April). Digital Transformation of Information Fulfillment and Patient Engagement for Health Service Safety. In Proceedings of the 4th International Conference on Management Science and Industrial Engineering (pp. 229-236).
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature medicine, 28(1), 31-38. 7. Harrer, S., Shah, P., Antony, B., & Hu, J. (2019). Artificial intelligence for clinical trial design. Trends in pharmacological sciences, 40(8), 577-591.
Blanco-Gonzalez, A., Cabezon, A., Seco-Gonzalez, A., Conde-Torres, D., Antelo-Riveiro, P., Pineiro, A., & Garcia-Fandino, R. (2023). The role of ai in drug discovery: challenges, opportunities, and strategies. Pharmaceuticals, 16(6), 891.
Klumpp, M., Hintze, M., Immonen, M., Ródenas-Rigla, F., Pilati, F., Aparicio-Martínez, F., & Delgado-Gonzalo, R. (2021, July). Artificial intelligence for hospital health care: Application cases and answers to challenges in European hospitals. In Healthcare (Vol. 9, No. 8, p. 961). MDPI.
Bravo, C., Saputelli, L., Rivas, F., Pérez, A. G., Nikolaou, M., Zangl, G., & Nunez, G. (2014). State of the art of artificial intelligence and predictive analytics in the E&P industry: a technology survey. Spe Journal, 19(04), 547-563.
Deepa, S. N., & Devi, B. A. (2011). A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology, 4(11), 1583-1595.
Levin, J. M., Oprea, T. I., Davidovich, S., Clozel, T., Overington, J. P., Vanhaelen, Q., & Zhavoronkov, A. (2020). Artificial intelligence, drug repurposing and peer review. Nature Biotechnology, 38(10), 1127-1131.
Alauddin, M. S., Baharuddin, A. S., & Mohd Ghazali, M. I. (2021, January). The modern and digital transformation of oral health care: A mini review. In Healthcare (Vol. 9, No. 2, p. 118). MDPI.
Nelson, G. S. (2019). Bias in artificial intelligence. North Carolina medical journal, 80(4), 220-222.
Roselli, D., Matthews, J., & Talagala, N. (2019, May). Managing bias in AI. In Companion Proceedings of the 2019 World Wide Web Conference (pp. 539-544).
Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligencedriven healthcare. In Artificial intelligence in healthcare (pp. 295-336). Academic Press.
Sunarti, S., Rahman, F. F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: opportunities and risk for future. Gaceta Sanitaria, 35, S67-S70.
Crouzier L, Richard EM, Sourbron J, Lagae L, Maurice T, Delprat B: Use of zebrafish models to boost research in rare genetic diseases. Int J Mol Sci. 2021, 22: 10.3390/ijms222413356
Papasavva P, Kleanthous M, Lederer CW: Rare opportunities: CRISPR/Cas-based therapy development for rare genetic diseases. Mol Diagn Ther. 2019, 23:201-22. 10.1007/s40291-019-00392-3
Brasil S, Pascoal C, Francisco R, Dos Reis Ferreira V, Videira PA, Valadão AG: Artificial intelligence (AI) in rare diseases: is the future brighter? Genes (Basel). 2019, 10:10.3390/genes10120978
Jamal A. 2023 Novel Approaches in the Field of Cancer Medicine. Biological times, 2(12): 52-53
Pavan S, Rommel K, Mateo Marquina ME, Höhn S, Lanneau V, Rath A: Clinical practice guidelines for rare diseases: the Orphanet database. PLoS One. 2017, 12:e0170365. 10.1371/journal.pone.0170365
Austin CP, Cutillo CM, and Lau LP, et al.: Future of Rare Diseases Research 2017-2027: an IRDiRC perspective. Clin Transl Sci. 2018, 11:21-7. 10.1111/cts.12500
Schee Genannt Halfmann S, Mählmann L, Leyens L, Reumann M, Brand A: Personalized medicine: What’s in it for rare diseases? 2017. 10.1007/978-3-319-67144-4_22
Khanna VV, Chadaga K, Sampathila N, Prabhu S, Chadaga R, Umakanth S: Diagnosing COVID-19 using artificial intelligence: a comprehensive review. Netw Model Anal Health Inform Bioinforma. 2022, 11:25. 10.1007/s13721-022-00367-1 2023 Abdallah et al. Cureus 15(10): e46860. DOI 10.7759/cureus.46860 7 of 9
Visibelli A, Roncaglia B, Spiga O, Santucci A: The impact of artificial intelligence in the Odyssey of rare diseases. Biomedicines. 2023, 11:10.3390/biomedicines11030887
Souza ÍP, Androlage JS, Bellato R, Barsaglini RA: A qualitative approach to rare genetic diseases: an integrative review of the national and international literature [Article in Portuguese]. Cien Saude Colet. 2019, 24:3683-700. 10.1590/1413-812320182410.17822019
Picci R, Oliva F, Trivelli F, et al.: Emotional burden and coping strategies of parents of children with rare diseases. J Child Fam Stud. 2013, 24: 10.1007/s10826-013-9864-5
Vinkšel M, Writzl K, Maver A, Peterlin B: Improving diagnostics of rare genetic diseases with NGS approaches. J Community Genet. 2021, 12:247-56. 10.1007/s12687-020-00500-5
Deng Y, Pan W: Significance testing for allelic heterogeneity. Genetics. 2018, 210:25-32. 10.1534/genetics.118.301111
Posey JE, O'Donnell-Luria AH, and Chong JX, et al.: Insights into genetics, human biology and disease gleaned from family based genomic studies. Genet Med. 2019, 21:798-812. 10.1038/s41436-018-0408-7
Di Resta C, Galbiati S, Carrera P, and Ferrari M: Next-generation sequencing approach for the diagnosis of human diseases: open challenges and new opportunities. EJIFCC. 2018, 29:4-14.
Jamuar SS, Tan EC: Clinical application of next-generation sequencing for Mendelian diseases. Hum Genomics. 2015, 9:10. 10.1186/s40246-015-0031-5
Reuter JA, Spacek DV, Snyder MP: High-throughput sequencing technologies. Mol Cell. 2015, 58:586-97. 10.1016/j.molcel.2015.05.004
Payne K, Gavan SP, Wright SJ, and Thompson AJ: Cost-effectiveness analyses of genetic and genomic diagnostic tests. Nat Rev Genet. 2018, 19:235-46. 10.1038/nrg.2017.108
Willmen T, Völkel L, Ronicke S, Hirsch MC, Kaufeld J, Rychlik RP, Wagner AD: Health economic benefits through the use of diagnostic support systems and expert knowledge. BMC Health Serv Res. 2021, 21:947. 10.1186/s12913-021-06926-y
Mueller T, Jerrentrup A, Bauer MJ, Fritsch HW, Schaefer JR: Characteristics of patients contacting a center for undiagnosed and rare diseases. Orphanet J Rare Dis. 2016, 11:81. 10.1186/s13023-016-0467-2
Tambuyzer E, Vandendriessche B, and Austin CP, et al.: Therapies for rare diseases: therapeutic modalities, progress and challenges ahead. Nat Rev Drug Discov. 2020, 19:93-111. 10.1038/s41573-019-0049-9
Hurvitz N, Azmanov H, Kesler A, Ilan Y: Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur J Hum Genet. 2021, 29:1485-90. 10.1038/s41431-021-00928-4
Carlson SF, Kamalia MA, Zimermann MT, Urrutia RA, Joyce DL: The current and future role of artificial intelligence in optimizing donor organ utilization and recipient outcomes in heart transplantation. Heart vessels transplant. 2022, 6:195-202. 10.24969/hvt.2022.350
Kilic A: Artificial Intelligence and Machine Learning in Cardiovascular Health Care. Ann Thorac Surg. 2020, 109:1323-9. 10.1016/j.athoracsur.2019.09.042
Khalsa RK, Khashkhusha A, Zaidi S, Harky A, Bashir M: Artificial intelligence and cardiac surgery during COVID-19 era. J Card Surg. 2021, 36:1729-33. 10.1111/jocs.15417
Goswami R: The current state of artificial intelligence in cardiac transplantation. Curr Opin Organ Transplant. 2021, 26:296-301. 10.1097/MOT.0000000000000875
Tseng PY, Chen YT, Wang CH, et al.: Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 2020, 24:478. 10.1186/s13054-020-03179-9
Zhou S, Wang L, Li G, et al. Decreased expression of receptor tyrosine kinase of EphB1 protein in renal cell carcinomas. Int J Clin Exp Pathol. 2014; 7(7):4254–4260.
Liu L, Wang X, Ge W. EphA8 is a prognostic factor for oral tongue squamous cell carcinoma. Med Sci Monit. 2018; 24:7213–7222.
Kina S, Kinjo T, Liang F, et al. Molecular and cellular pharmacology Targeting EphA4 abrogates intrinsic resistance to chemotherapy in well-differentiated cervical cancer cell line. Eur J Pharmacol 2018; 840, 70-78.
Giaginis C, Tsourouflis G, Zizi-Serbetzoglou A, et al. Clinical significance of ephrin (eph)-a1, -a2, -a4, -a5 and -a7 receptors in pancreatic ductal adenocarcinoma. Pathol Oncol Res. 2010; 16 (2):267–276.
Hafner C, Becker B, Landthaler M, et al. Expression profile of Eph receptors and ephrin ligands in human skin and downregulation of EphA1 in nonmelanoma skin cancer. Mod Pathol. 2006; 19 (10):1369–1377.
Wang Y, Yu H, Shan Y, et al. EphA1 activation promotes the homing of endothelial progenitor cells to hepatocellular carcinoma for tumor neovascularization through the SDF-1/CXCR4 signaling pathway. J Exp Clin Cancer Res. 2016; 35(1):65.
Herath NI, Doecke J, Spanevello MD, et al. Epigenetic silencing of EphA1 expression in colorectal cancer is correlated with poor survival. Br J Cancer. 2009; 100(7):1095–1102.
Yamazaki T, Masuda J, Omori T, et al. EphA1 interacts with integrin-linked kinase and regulates cell morphology and motility. J Cell Sci. 2009; 122(2):243–255.
Larsen AB, Stockhausen MT, Poulsen HS. Cell adhesion and EGFR activation regulate EphA2 expression in cancer. Cell Signal. 2010; 22 (4):636–644.
Miao H, Li DQ, Mukherjee A, et al. EphA2 mediatesligand-dependent inhibition and ligand-independent promotion of cell migration and invasion via a reciprocal regulatory loop with Akt. Cancer Cell. 2009; 16(1):9–20. •• Provides an explanation for the difference between ligand dependent and ligand independent signalling of the EphA2 receptor.
Collins GS, Reitsma JB, Altman DG, and Moons KG: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Circulation. 2015, 131:211-9. 10.1161/CIRCULATIONAHA.114.014508
Vickers AJ, Cronin AM, and Elkin EB, Gonen M: Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008, 8:53. 10.1186/1472-6947-8-53
Leung R: Using AI-ML to augment the capabilities of social media for telehealth and remote patient monitoring. Healthcare (Basel). 2023, 11:10.3390/healthcare11121704
Tassa T, Cohen DJ: Anonymization of centralized and distributed social networks by sequential clustering. IEEE Trans Knowl Data Eng. 2013, 25:311-24. 10.1109/TKDE.2011.232
Iqbal J, Cortés Jaimes DC, Makineni P, et al.: Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus. 2023, 15:e44658. 10.7759/cureus.44658
Hamaoka Y, Negishi M, Katoh H. EphA2 is a key effector of the MEK/ERK/RSK pathway regulating glioblastoma cell proliferation; 2016. DOI:10.1016/j.cellsig.2016.04.009.
Li JY, Xiao T, Yi HM, et al. S897 phosphorylation of EphA2 is indispensable for EphA2-dependent nasopharyngeal carcinoma cell invasion, metastasis and stem properties. Cancer Lett. 2019; 444:162–174.
Duxbury MS, Ito H, Zinner MJ, et al. EphA2: A determinant of malignant cellular behavior and a potential therapeutic target in pancreatic adenocarcinoma. Oncogene. 2004; 23(7):1448–1456.
Yeddula N, Xia Y, Ke E, et al. screening for tumor suppressors: loss of Ephrin receptor A2 cooperates with oncogenic KRas in promoting lung adenocarcinoma. Proc Natl Acad Sci U S A. 2015; 112(47): E6476–E6485.
Peng J, Wang Q, Liu H, et al. EPHA3 regulates the multidrug resistance of small cell lung cancer via the PI3K/BMX/STAT3 signaling pathway. Tumor Biol. 2016;37(9):11959–11971.
Zhuang G, Song W, Amato K, et al. Effects of cancer-associated EPHA3 mutations on lung cancer. J Natl Cancer Inst. 2012; 104 (15):1182–1197.
