The Intersection of Artificial Intelligence in Public Health and Personalized Cancer Therapy

Editorial

This editorial aims to highlight the critical intersections of patient care and technology, as illustrated in our recent article collection and addresses urgent challenges posed by global health crises like the COVID-19 pandemic while exploring broader themes such as personalized medicine, ethical practices, and the nutritional impacts on health. This diverse range of research highlights the necessity of interdisciplinary approaches to address the complexities of modern healthcare.

Mario, et al. explored how high public debt affects a country’s capacity to manage healthcare expenditures and fatality rates during global emergencies like the COVID-19 pandemic [11Mario C. Country risk to face global emergencies: negative effects of high public debt on health expenditures and fatality rate in COVID-19 pandemic crisis. IgMin Res. 2024;2:537-45.]. The study reveals that high public debt severely undermines healthcare and socioeconomic systems, rendering them more susceptible to crises. Statistical analysis indicates that rising public debt correlates with declining health expenditures over time, which heightens the risk and vulnerability rate of countries, particularly Europe. Notably, a 1% increase in healthcare spending per capita is linked to a 1.2% decrease in COVID-19 fatality rates. These findings emphasize the necessity of reducing public debt through effective governance and robust institutions while safeguarding resources for healthcare. Several lines of evidence have shown that Artificial Intelligence (AI) models show good prediction performance for COVID-19 hospitalization and mortality [22Shakibfar S, Nyberg F, Li H, Zhao J, Nordeng HME, Sandve GKF, Pavlovic M, Hajiebrahimi M, Andersen M, Sessa M. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Front Public Health. 2023 Jun 20;11:1183725. doi: 10.3389/fpubh.2023.1183725. PMID: 37408750; PMCID: PMC10319067.], particularly in helping to effectively detect and forecast COVID-19 [33Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. Sensors (Basel). 2023 Jan 3;23(1):527. doi: 10.3390/s23010527. PMID: 36617124; PMCID: PMC9824505.].

Additionally, AI can potentially help predict mortality, detect, screen, and trace patients, analyze health data, prioritize high-risk patients, and allocate hospital resources during pandemics [44Ahmadi Marzaleh M, Peyravi M, Mousavi S, Sarpourian F, Seyedi M, Shalyari N. Artificial Intelligence Functionalities During the COVID-19 Pandemic. Disaster Med Public Health Prep. 2023 Feb 27;17:e336. doi: 10.1017/dmp.2023.3. PMID: 36847255.]; particularly in areas like data quality and diversity limitations [55Farhat F, Sohail SS, Alam MT, Ubaid S, Shakil, Ashhad M, Madsen DØ. COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control. Front Artif Intell. 2023 Nov 8;6:1266560. doi: 10.3389/frai.2023.1266560. PMID: 38028660; PMCID: PMC10663297.]; leading to better scale-up, timely response, and reliable, efficient outcomes, sometimes outperforming humans in certain healthcare tasks [66Khan M, Mehran MT, Haq ZU, Ullah Z, Naqvi SR, Ihsan M, Abbass H. Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Syst Appl. 2021 Dec 15;185:115695. doi: 10.1016/j.eswa.2021.115695. Epub 2021 Aug 4. PMID: 34400854; PMCID: PMC8359727.,77Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne). 2021 Sep 30;8:704256. doi: 10.3389/fmed.2021.704256. PMID: 34660623; PMCID: PMC8514781.] and can effectively detect clusters of COVID-19 cases and predict future outbreaks, aiding healthcare organizations in real-time disease management and vaccine development [88Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul-Aug;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012. Epub 2020 Apr 14. PMID: 32305024; PMCID: PMC7195043.]. Imad, et al. reported that AI has the potential to enhance patient outcomes, lower costs, and boost efficiency [99Imad-Addin A. The power of artificial intelligence for improved patient outcomes, ethical practices, and overcoming challenges. IgMin Res. 2024;2:597-600.], while also acknowledging ethical challenges like data privacy and algorithmic bias. Qazi, et al. reported that the application of Convolutional Neural Networks (CNNs) for classifying pneumonia and COVID-19 using chest X-ray images, showed that CNNs alone achieve a remarkable recall rate of 99.47% for pneumonia detection and an accuracy of 95.45% for distinguishing COVID-19 cases [1010Qazi Waqas K. A machine learning-based method for COVID-19 and pneumonia detection. IgMin Res. 2024;2:518-23.]. In addition to the COVID-19 disease, the editorial has covered other diseases like non-cardioembolic ischemic stroke and cancer as well. Studies have shown that Cardioembolic stroke is the most common subtype of stroke in COVID-19 patients, making it an uncommon manifestation in our centers [1111Tajmirriahi M, Masjedi Esfahani M, Amouaghaei Z, Mansori N, Miralaei P, Lalehzar SS, Shirani P, Saadatnia M. Cardioembolic stroke, the most common subtype of stroke in COVID 19: A single center experience from Isfahan, Iran. J Res Med Sci. 2023 Feb 21;28:10. doi: 10.4103/jrms.jrms_594_21. PMID: 36974106; PMCID: PMC10039102.], particularly recovered COVID-19 patients have a higher risk of ischemic stroke compared to the general population within 9 months of the infection [1212Zuin M, Mazzitelli M, Rigatelli G, Bilato C, Cattelan AM. Risk of ischemic stroke in patients recovered from COVID-19 infection: A systematic review and meta-analysis. Eur Stroke J. 2023 Dec;8(4):915-922. doi: 10.1177/23969873231190432. Epub 2023 Jul 25. PMID: 37491810; PMCID: PMC10372514.] and have worse outcomes, with nearly fourfold increased mortality, half the odds of mRS scores 0–2, and one-third the odds of home discharge [1313Ferrone SR, Sanmartin MX, Ohara J, Jimenez JC, Feizullayeva C, Lodato Z, Shahsavarani S, Lacher G, Demissie S, Vialet JM, White TG, Wang JJ, Katz JM, Sanelli PC. Acute ischemic stroke outcomes in patients with COVID-19: a systematic review and meta-analysis. J Neurointerv Surg. 2024 Mar 14;16(4):333-341. doi: 10.1136/jnis-2023-020489. PMID: 37460215.,1414Libruder C, Hershkovitz Y, Ben-Yaish S, Tanne D, Keinan-Boker L, Binyaminy B. An Increased Risk for Ischemic Stroke in the Short-Term Period following COVID-19 Infection: A Nationwide Population-Based Study. Neuroepidemiology. 2023;57(4):253-259. doi: 10.1159/000531163. Epub 2023 Jul 3. PMID: 37399799; PMCID: PMC11251667.]. Anastasia, et al. have reported that aspirin resistance and its links to genetic markers and clinical parameters in patients with non-cardioembolic ischemic stroke by using a machine learning model which underscores the importance of combining genetic and clinical data for personalized prevention strategies [1515Anastasia V, Sergey S, Anastasia S, Tatyana V, Igor V. The model for clinical, laboratory, and genetic prediction of recurrent ischemic stroke against the background of laboratory aspirin resistance using machine learning. IgMin Res. 2024;2:39-44.], other studies have shown that aspirin-treated coronary artery disease patients, laboratory aspirin resistance predicts death and target vessel revascularization and is associated with stroke recurrence [1616Roman-Gonzalez A, Naranjo CA, Cardona-Maya WD, Vallejo D, Garcia F, Franco C, Alvarez L, Tobón LI, López MI, Rua C, Bedoya G, Cadavid Á, Torres JD. Frequency of Aspirin Resistance in Ischemic Stroke Patients and Healthy Controls from Colombia. Stroke Res Treat. 2021 May 21;2021:9924710. doi: 10.1155/2021/9924710. PMID: 34094500; PMCID: PMC8164531.].

Furthermore, this editorial covered recent studies about different cancers like Darya Sitovskaya, et al. investigated the histological features of the stroma in high-grade gliomas, particularly focusing on isocitrate dehydrogenase 1 (IDH1) gene mutations. Their study highlights differences in collagen synthesis and stromal changes between IDH-mutant astrocytomas and IDH-wildtype glioblastomas, which may complicate treatment efforts [1717Darya S, Ksenia F, Elizaveta S, Tatyana S, Yulia Z. Study of the histological features of the stroma of high-grade gliomas depending on the status of the mutation in the IDH1 gene. IgMin Res. 2024;2:702-8.]. Other studies have reported that IDH1 mutation status is more important for overall survival than histological diagnosis in high-grade astrocytic gliomas, with younger patients having more IDH1 mutations [1818Richardson TE, Hatanpaa KJ, Walker JM. Molecular Characterization of "True" Low-Grade IDH-Wildtype Astrocytomas. J Neuropathol Exp Neurol. 2021 Apr 16;80(5):431-435. doi: 10.1093/jnen/nlab023. PMID: 33829259.] and histological grading and molecular profiling are crucial for prognostic stratification of IDH-wildtype grade II gliomas, with caution when comparing them to molecular glioblastomas [1919Álvarez-Torres MDM, López-Cerdán A, Andreu Z, de la Iglesia Vayá M, Fuster-Garcia E, García-García F, García-Gómez JM. Vascular differences between IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 at imaging and transcriptomic levels. NMR Biomed. 2023 Nov;36(11):e5004. doi: 10.1002/nbm.5004. Epub 2023 Jul 21. PMID: 37482922.].

Ionuţ, et al. addressed the challenges of diagnosing pancreatic tumors, highlighting the limitations of current imaging techniques and underscoring the importance of developing new diagnostic procedures and biomarkers to facilitate earlier detection and improve patient outcomes [2020Simion CI, Elena Violeta C, Costin George F, Teodora Elena T, Cosmin B, Anwar E, et al. Diagnostic challenges in pancreatic tumors. IgMin Res. 2024;2:348-53.]. Other studies have explored that early detection of pancreatic cancer is crucial, and current imaging techniques, such as Magnetic Resonance Imaging (MRI) with Magnetic Resonance Cholangiopancreatography (MRCP), Immuno- Positron Emission Tomography (PET), dual-energy- Computed Tomography (CT), offers a non-invasive detection and help make optimal treatment decisions [2121González-Gómez R, Pazo-Cid RA, Sarría L, Morcillo MÁ, Schuhmacher AJ. Diagnosis of Pancreatic Ductal Adenocarcinoma by Immuno-Positron Emission Tomography. J Clin Med. 2021 Mar 10;10(6):1151. doi: 10.3390/jcm10061151. PMID: 33801810; PMCID: PMC8000738.] particularly staging of pancreatic carcinoma [2222Miura F, Takada T, Amano H, Yoshida M, Furui S, Takeshita K. Diagnosis of pancreatic cancer. HPB (Oxford). 2006;8(5):337-42. doi: 10.1080/13651820500540949. PMID: 18333085; PMCID: PMC2020745.]. Furthermore, Andrea, et al. presented a case report on a patient with epithelial ovarian cancer, advocating for the integration of Hyperthermic Intraperitoneal Chemotherapy (HIPEC) into treatment regimens. The findings suggest that HIPEC may improve patient outcomes when combined with cytoreductive surgery [2323González DG, Asensio DE, Pinto FP, Pérez SB, Pacheco SD. Peritoneal carcinomatosis from ovarian cancer: a case report. IgMin Res. 2024;2:309-12.]. Other reports have shown that HIPEC combined with cytoreductive surgery prolongs overall survival and progression-free survival in advanced epithelial ovarian cancer patients [2424Della Corte L, Conte C, Palumbo M, Guerra S, Colacurci D, Riemma G, De Franciscis P, Giampaolino P, Fagotti A, Bifulco G, Scambia G. Hyperthermic Intraperitoneal Chemotherapy (HIPEC): New Approaches and Controversies on the Treatment of Advanced Epithelial Ovarian Cancer-Systematic Review and Meta-Analysis. J Clin Med. 2023 Nov 9;12(22):7012. doi: 10.3390/jcm12227012. PMID: 38002626; PMCID: PMC10672052.] especially improving recurrence-free survival and overall survival in stage III epithelial ovarian cancer patients without increasing side effects [2525van Driel WJ, Koole SN, Sikorska K, Schagen van Leeuwen JH, Schreuder HWR, Hermans RHM, de Hingh IHJT, van der Velden J, Arts HJ, Massuger LFAG, Aalbers AGJ, Verwaal VJ, Kieffer JM, Van de Vijver KK, van Tinteren H, Aaronson NK, Sonke GS. Hyperthermic Intraperitoneal Chemotherapy in Ovarian Cancer. N Engl J Med. 2018 Jan 18;378(3):230-240. doi: 10.1056/NEJMoa1708618. PMID: 29342393.]. Fabrizio, et al. explored strategies to mitigate rectal toxicity in prostate cancer patients undergoing radiotherapy and suggests that a fiber-/fat-free diet combined with activated charcoal and macrogol may reduce toxicity [2626Fabrizio P, Daria C, Ugo P, Gabriella T, Luigi M. Preventing rectal toxicity in prostate cancer: diet and supplement alternative to enemas or rectal spacer. IgMin Res. 2024;2:171-6.]. Additionally, other reports have demonstrated that a low FODMAP diet significantly decreased rectal gas and rectal volume during prostate cancer radiotherapy, with excellent patient compliance and contentment [2727Schaefer C, Zamboglou C, Volegova-Neher N, Martini C, Nicolay NH, Schmidt-Hegemann NS, Rogowski P, Li M, Belka C, Müller AC, Grosu AL, Brunner T. Impact of a low FODMAP diet on the amount of rectal gas and rectal volume during radiotherapy in patients with prostate cancer - a prospective pilot study. Radiat Oncol. 2020 Jan 30;15(1):27. doi: 10.1186/s13014-020-1474-y. PMID: 32000818; PMCID: PMC6993432.], and tuning constraints to individual patient characteristics and effectively reduced rectal morbidity in prostate cancer radiotherapy improving their quality of life [2828Valdagni R, Rancati T. Reducing rectal injury during external beam radiotherapy for prostate cancer. Nat Rev Urol. 2013 Jun;10(6):345-57. doi: 10.1038/nrurol.2013.96. Epub 2013 May 14. PMID: 23670182.]. Zaira, et al. highlighted the critical issue of breast cancer, emphasizing the importance of early detection and the role of multidisciplinary teams (MDTs) in providing comprehensive care [2929Zaira G, Asia G. Breast cancer: the road to a personalized prevention. IgMin Res. 2024;2:163-70.]. Other reports have shown that MDTs involving specialists in imaging, pathology, molecular diagnostics, and therapeutics for cancer care improve patient outcomes and decision-making, leading to better survival rates and enhanced cancer management [3030Prades J, Remue E, van Hoof E, Borras JM. Is it worth reorganising cancer services on the basis of multidisciplinary teams (MDTs)? A systematic review of the objectives and organisation of MDTs and their impact on patient outcomes. Health Policy. 2015 Apr;119(4):464-74. doi: 10.1016/j.healthpol.2014.09.006. Epub 2014 Sep 18. PMID: 25271171.], particularly increasing staging completion, encouraging neo-adjuvant treatment, and creating a positive work environment for team members [3131Capobianco AML, Gallucci G, Lapadula L, Dinardo G, La Torre G, Sisti N, Sisti LG. Impact of the Multidisciplinary Cancer Team on the Diagnostic and Therapeutic Care Pathway of Early Breast Cancer Patients and Perception of Team Members: The Experience of a Cancer Centre in Italy. Cancer Invest. 2024 Jan;42(1):12-20. doi: 10.1080/07357907.2023.2300442. Epub 2024 Jan 22. PMID: 38149612.] is crucial for optimal patient care and outcomes in early-stage breast cancer [3232Lyman GH, Baker J, Geradts J, Horton J, Kimmick G, Peppercorn J, Pruitt S, Scheri RP, Hwang ES. Multidisciplinary care of patients with early-stage breast cancer. Surg Oncol Clin N Am. 2013 Apr;22(2):299-317. doi: 10.1016/j.soc.2012.12.005. Epub 2013 Jan 3. PMID: 23453336.], not only breast cancer but also, other cancer like lung, colorectal, prostate cancer [3333Kočo L, Weekenstroo HHA, Lambregts DMJ, Sedelaar JPM, Prokop M, Fütterer JJ, Mann RM. The Effects of Multidisciplinary Team Meetings on Clinical Practice for Colorectal, Lung, Prostate and Breast Cancer: A Systematic Review. Cancers (Basel). 2021 Aug 18;13(16):4159. doi: 10.3390/cancers13164159. PMID: 34439312; PMCID: PMC8394238.], but long-term clinical effects require further evaluation.

Amal, et al. have reported the antidepressant and antioxidant potential of protein extracts from beans and eggs, focusing on both denatured and non-denatured forms by using mouse models [3434El Hamsas EY, Omaima B, Zineb S, Najat EA, Driss R. The antioxidant and antidepressant properties of dietary proteins derived from egg and bean extracts and their acute toxicity: a journey from nutrition to pharmacognosy. IgMin Res. 2023;1:032-42.]. Significant antidepressant effects were noted, particularly at 80% saturation. Studies have shown that antidepressants may cause, promote, or inhibit cancers, with amitriptyline showing a transient positive association with liver cancer and pancreatic cancer, and others potentially increasing breast cancer risk [3535Steingart AB, Cotterchio M. Do antidepressants cause, promote, or inhibit cancers? J Clin Epidemiol. 1995 Nov;48(11):1407-12. doi: 10.1016/0895-4356(95)00545-5. PMID: 7490604.], decreasing the incidence risk of ovarian cancer [3636Zhuang Y, Pang X, Qi Y, Zhang T, Cao G, Xue H, Xu Y, Xie S, Liu Y, Wang Y, Li Y, Xiong Y, Li Y, Shen H. The incidence risk of breast and gynecological cancer by antidepressant use: A systematic review and dose-response meta-analysis of epidemiological studies involving 160,727 patients. Front Oncol. 2022 Oct 14;12:939636. doi: 10.3389/fonc.2022.939636. PMID: 36425551; PMCID: PMC9680975.], Antidepressants with anti-tumor effects, such as Tricyclic antidepressants (TCAs), Selective Serotonin Reuptake Inhibitors (SSRIs), and Monoamine Oxidase Inhibitors (MAOIs), show potential in treating glioblastoma by reducing adverse effects from chemo-radiotherapy and synergizing with chemotherapy [3737Abadi B, Shahsavani Y, Faramarzpour M, Rezaei N, Rahimi HR. Antidepressants with anti-tumor potential in treating glioblastoma: A narrative review. Fundam Clin Pharmacol. 2022 Feb;36(1):35-48. doi: 10.1111/fcp.12712. Epub 2021 Jul 29. PMID: 34212424.]. Antioxidant therapy targeting oxidative stress in cancer treatment shows potential, particularly in targeting reactive oxygen species scavengers, which increase cancer cells' antioxidant capacity [3838Luo M, Zhou L, Huang Z, Li B, Nice EC, Xu J, Huang C. Antioxidant Therapy in Cancer: Rationale and Progress. Antioxidants (Basel). 2022 Jun 8;11(6):1128. doi: 10.3390/antiox11061128. PMID: 35740025; PMCID: PMC9220137.]. Therefore, understanding their mechanisms and targeting tumor cells' antioxidant capacity may have a positive therapeutic impact by modulating oxidative stress levels, which may potentially prevent tumor development and improve anticancer therapy responses.

In conclusion, the studies discussed in this editorial underscore the critical importance of collaboration across various research disciplines, particularly in areas like personalized medicine, artificial intelligence, and ethical healthcare practices. Future research should continue to investigate emerging therapies, such as antioxidant treatments while prioritizing data privacy and AI ethics through interdisciplinary approaches and the management of global health crises. This will ultimately contribute to building a more resilient healthcare system for future generations.

The perspective of using AI-based big data models can help clinicians understand tumors and develop targeted treatment strategies [3939Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol. 2023 Jan 4;12:998222. doi: 10.3389/fonc.2022.998222. PMID: 36686757; PMCID: PMC9846804.] with the integration of genomic data and drug-response data [4040Rezayi S, R Niakan Kalhori S, Saeedi S. Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. Biomed Res Int. 2022 Apr 7;2022:7842566. doi: 10.1155/2022/7842566. PMID: 35434134; PMCID: PMC9010213.,4141Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021 Apr;11(4):900-915. doi: 10.1158/2159-8290.CD-21-0090. PMID: 33811123; PMCID: PMC8034385.]; and improve clinical validation, which potentially leads to practice-changing cancer therapy [4242Ho D. Artificial intelligence in cancer therapy. Science. 2020 Feb 28;367(6481):982-983. doi: 10.1126/science.aaz3023. PMID: 32108102.] and enhance cancer diagnosis and treatment by enhancing early detection, diagnosis, classification, grading, and personalized treatment [4343Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021 Nov;41(11):1100-1115. doi: 10.1002/cac2.12215. Epub 2021 Oct 6. PMID: 34613667; PMCID: PMC8626610.]. One example is in using AI-based models in brain tumor imaging, accelerating the shift towards patient-tailored medicine, and improving diagnostics, therapeutic options, and surgical planning [4444Cè M, Irmici G, Foschini C, Danesini GM, Falsitta LV, Serio ML, Fontana A, Martinenghi C, Oliva G, Cellina M. Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine. Curr Oncol. 2023 Feb 22;30(3):2673-2701. doi: 10.3390/curroncol30030203. PMID: 36975416; PMCID: PMC10047107.].

References

1. Mario C. Country risk to face global emergencies: negative effects of high public debt on health expenditures and fatality rate in COVID-19 pandemic crisis. IgMin Res. 2024;2:537-45.

2. Shakibfar S, Nyberg F, Li H, Zhao J, Nordeng HME, Sandve GKF, Pavlovic M, Hajiebrahimi M, Andersen M, Sessa M. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Front Public Health. 2023 Jun 20;11:1183725. doi: 10.3389/fpubh.2023.1183725. PMID: 37408750; PMCID: PMC10319067.

3. Hasan MM, Islam MU, Sadeq MJ, Fung WK, Uddin J. Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment. Sensors (Basel). 2023 Jan 3;23(1):527. doi: 10.3390/s23010527. PMID: 36617124; PMCID: PMC9824505.

4. Ahmadi Marzaleh M, Peyravi M, Mousavi S, Sarpourian F, Seyedi M, Shalyari N. Artificial Intelligence Functionalities During the COVID-19 Pandemic. Disaster Med Public Health Prep. 2023 Feb 27;17:e336. doi: 10.1017/dmp.2023.3. PMID: 36847255.

5. Farhat F, Sohail SS, Alam MT, Ubaid S, Shakil, Ashhad M, Madsen DØ. COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control. Front Artif Intell. 2023 Nov 8;6:1266560. doi: 10.3389/frai.2023.1266560. PMID: 38028660; PMCID: PMC10663297.

6. Khan M, Mehran MT, Haq ZU, Ullah Z, Naqvi SR, Ihsan M, Abbass H. Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert Syst Appl. 2021 Dec 15;185:115695. doi: 10.1016/j.eswa.2021.115695. Epub 2021 Aug 4. PMID: 34400854; PMCID: PMC8359727.

7. Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne). 2021 Sep 30;8:704256. doi: 10.3389/fmed.2021.704256. PMID: 34660623; PMCID: PMC8514781.

8. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul-Aug;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012. Epub 2020 Apr 14. PMID: 32305024; PMCID: PMC7195043.

9. Imad-Addin A. The power of artificial intelligence for improved patient outcomes, ethical practices, and overcoming challenges. IgMin Res. 2024;2:597-600.

10. Qazi Waqas K. A machine learning-based method for COVID-19 and pneumonia detection. IgMin Res. 2024;2:518-23.

11. Tajmirriahi M, Masjedi Esfahani M, Amouaghaei Z, Mansori N, Miralaei P, Lalehzar SS, Shirani P, Saadatnia M. Cardioembolic stroke, the most common subtype of stroke in COVID 19: A single center experience from Isfahan, Iran. J Res Med Sci. 2023 Feb 21;28:10. doi: 10.4103/jrms.jrms_594_21. PMID: 36974106; PMCID: PMC10039102.

12. Zuin M, Mazzitelli M, Rigatelli G, Bilato C, Cattelan AM. Risk of ischemic stroke in patients recovered from COVID-19 infection: A systematic review and meta-analysis. Eur Stroke J. 2023 Dec;8(4):915-922. doi: 10.1177/23969873231190432. Epub 2023 Jul 25. PMID: 37491810; PMCID: PMC10372514.

13. Ferrone SR, Sanmartin MX, Ohara J, Jimenez JC, Feizullayeva C, Lodato Z, Shahsavarani S, Lacher G, Demissie S, Vialet JM, White TG, Wang JJ, Katz JM, Sanelli PC. Acute ischemic stroke outcomes in patients with COVID-19: a systematic review and meta-analysis. J Neurointerv Surg. 2024 Mar 14;16(4):333-341. doi: 10.1136/jnis-2023-020489. PMID: 37460215.

14. Libruder C, Hershkovitz Y, Ben-Yaish S, Tanne D, Keinan-Boker L, Binyaminy B. An Increased Risk for Ischemic Stroke in the Short-Term Period following COVID-19 Infection: A Nationwide Population-Based Study. Neuroepidemiology. 2023;57(4):253-259. doi: 10.1159/000531163. Epub 2023 Jul 3. PMID: 37399799; PMCID: PMC11251667.

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16. Roman-Gonzalez A, Naranjo CA, Cardona-Maya WD, Vallejo D, Garcia F, Franco C, Alvarez L, Tobón LI, López MI, Rua C, Bedoya G, Cadavid Á, Torres JD. Frequency of Aspirin Resistance in Ischemic Stroke Patients and Healthy Controls from Colombia. Stroke Res Treat. 2021 May 21;2021:9924710. doi: 10.1155/2021/9924710. PMID: 34094500; PMCID: PMC8164531.

17. Darya S, Ksenia F, Elizaveta S, Tatyana S, Yulia Z. Study of the histological features of the stroma of high-grade gliomas depending on the status of the mutation in the IDH1 gene. IgMin Res. 2024;2:702-8.

18. Richardson TE, Hatanpaa KJ, Walker JM. Molecular Characterization of "True" Low-Grade IDH-Wildtype Astrocytomas. J Neuropathol Exp Neurol. 2021 Apr 16;80(5):431-435. doi: 10.1093/jnen/nlab023. PMID: 33829259.

19. Álvarez-Torres MDM, López-Cerdán A, Andreu Z, de la Iglesia Vayá M, Fuster-Garcia E, García-García F, García-Gómez JM. Vascular differences between IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 at imaging and transcriptomic levels. NMR Biomed. 2023 Nov;36(11):e5004. doi: 10.1002/nbm.5004. Epub 2023 Jul 21. PMID: 37482922.

20. Simion CI, Elena Violeta C, Costin George F, Teodora Elena T, Cosmin B, Anwar E, et al. Diagnostic challenges in pancreatic tumors. IgMin Res. 2024;2:348-53.

21. González-Gómez R, Pazo-Cid RA, Sarría L, Morcillo MÁ, Schuhmacher AJ. Diagnosis of Pancreatic Ductal Adenocarcinoma by Immuno-Positron Emission Tomography. J Clin Med. 2021 Mar 10;10(6):1151. doi: 10.3390/jcm10061151. PMID: 33801810; PMCID: PMC8000738.

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Rashid M. The Intersection of Artificial Intelligence in Public Health and Personalized Cancer Therapy. IgMin Res.. November 06, 2024; 2(11): 922-925. IgMin ID: igmin268; DOI:10.61927/igmin268; Available at: igmin.link/p268

• DOI10.61927/igmin268

26 Sep, 2024

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05 Nov, 2024

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06 Nov, 2024

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