ac

Synergizing AI and Healthcare: Pioneering Advances in Cancer Medicine for Personalized Treatment

Authors

  • Abdul Mannan Khan Sherani Washington University of Science and Technology, Virginia
  • Murad Khan American National University, Salem Virginia
  • Muhammad Umer Qayyum Washington University of Science and Technology, Virginia
  • Hafiz Khawar Hussain DePaul University Chicago, Illinois

DOI:

10.47709/ijmdsa.v3i01.3562

Keywords:

Key words: Virtual tumor boards, liquid biopsies, drug discovery, challenges, ethical concerns, patient privacy, regulatory frameworks, collaborative efforts, healthcare integration, personalized cancer therapies, AI, AI, cancer medicine, transformative role, diagnosis, smart revolution, treatment optimization, predictive modeling, continuous monitoring, post-treatment care, innovation

Dimension Badge Record



Abstract

This paper investigates how Artificial Intelligence (AI) is changing the field of cancer medicine. It is organized into nine major sections that illustrate the profound effects of AI on different aspects of cancer care. Starting from the early phases of the disease, AI shows how it can transform conventional diagnostic methods by providing quick and accurate analyses of medical imaging, pathology slides, and genetic data. The paper then goes into the era of personalized cancer therapies, highlighting the ways in which AI helps to customize treatment based on individual genetic and molecular profiles. Finally, the paper discusses the smart revolution in healthcare, which is driven by AI integration, highlighting the impact of AI on diagnosis precision, treatment optimization, and resource allocation. Moreover, the story delves into how AI is being incorporated into healthcare outside of diagnosis and treatment, including areas like predictive modeling, ongoing monitoring, and after-treatment care. AI has the capacity to revolutionize cancer medicine by improving current practices and fostering innovation in clinical research, diagnosis modalities, and treatment planning. The paper highlights the revolutionary boundaries that AI has created, including liquid biopsies, virtual tumor boards, and the speeding up of drug discovery processes. The narrative weaves a thorough overview of AI's transformative journey in cancer care, offering insights into its current impact and the promising possibilities that lie ahead.

Google Scholar Cite Analysis
Abstract viewed = 418 times

References

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017, 542:115-8. 10.1038/nature21056

Davenport T, Kalakota R: The potential for artificial intelligence in healthcare. Future Healthc J. 2019, 6:94- 8. 10.7861/futurehosp.6-2-94

Lee SI, Celik S, Logsdon BA, et al.: A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat Commun. 2018, 9:42. 10.1038/s41467-017-02465-5

Fakoor R, Ladhak F, Nazi A, and Huber M: Using deep learning to enhance cancer diagnosis and classification. Proceedings of the ICML Workshop on the Role of Machine Learning in Transforming Healthcare. 2013,

Kondra S: Research in artificial intelligence for rare genetic diseases. ThinkGenetic INC. 2021, 10.13140/RG.2.2.22811.39208

Lee YS, Krishnan A, Oughtred R, et al.: A computational framework for genome-wide characterization of the human disease landscape. Cell Syst. 2019, 8:152-162.e6. 10.1016/j.cels.2018.12.010

Carlier A, Vasilevich A, Marechal M, de Boer J, Geris L: In silico clinical trials for pediatric orphan diseases. Sci Rep. 2018, 8:2465. 10.1038/s41598-018-20737-y

Kitsios F, Kamariotou M, Syngelakis AI, Talias MA: Recent advances of artificial intelligence in healthcare: a systematic literature review. Appl Sci. 2023, 13:7479. 10.3390/app13137479

Schaefer J, Lehne M, Schepers J, Prasser F, Thun S: The use of machine learning in rare diseases: a scoping review. Orphanet J Rare Dis. 2020, 15:145. 10.1186/s13023-020-01424-6

Ronicke S, Hirsch MC, Türk E, Larionov K, Tientcheu D, Wagner AD: Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J Rare Dis. 2019, 14:69. 10.1186/s13023-019-1040-6

Gurovich Y, Hanani Y, and Bar O, et al.: Identifying facial phenotypes of genetic disorders using deep learning. Nat Med. 2019, 25:60-4. 10.1038/s41591-018-0279-0

Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA: Artificial Intelligence: Review of current and future applications in medicine. Fed Pract. 2021, 38:527-38. 10.12788/fp.0174 40. Shimizu H, Nakayama KI: Artificial intelligence in oncology. Cancer Sci. 2020, 111:1452-60. 10.1111/cas.14377

Donation D, Terry SF: The application of artificial intelligence in the diagnosis of cancer and rare genetic diseases. Genet Test Mol Biomarkers. 2023, 27:203-4. 10.1089/gtmb.2023.29074.persp

Xu J, Yang P, Xue S, et al.: Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet. 2019, 138:109-24. 10.1007/s00439-019-01970- 5 2023 Abdallah et al. Cureus 15(10): e46860. DOI 10.7759/cureus.46860 8 of 9

Faviez C, Chen X, Garcelon N, et al.: Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis. 2020, 15:94. 10.1186/s13023-020-01374-z

Tabor HK, Goldenberg A: What precision medicine can learn from rare genetic disease research and translation? AMA J Ethics. 2018, 20:E834-840. 10.1001/amajethics.2018.834

Foksinska A, Crowder CM, Crouse AB, et al.: The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren. Front Artif Intell. 2022, 5:910216. 10.3389/frai.2022.910216

Wojtara M, Rana E, Rahman T, Khanna P, Singh H: Artificial intelligence in rare disease diagnosis and treatment. Clin Transl Sci. 2023, 10.1111/cts.13619

Alves VM, Korn D, Pervitsky V, et al.: Knowledge-based approaches to drug discovery for rare diseases. Drug Discov Today. 2022, 27:490-502. 10.1016/j.drudis.2021.10.014

Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L, Qiu Y: Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J Int Med Res. 2021, 49:3000605211000157. 10.1177/03000605211000157

Mittelstadt BD, Floridi L: The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics. 2016, 22:303-41. 10.1007/s11948-015-9652-2

Rajkomar A, Hardt M, Howell MD, Corrado G, and Chin MH: Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018, 169:866-72. 10.7326/M18-1990

Tobia K, Nielsen A, Stremitzer A: When Does Physician Use of AI Increase Liability? . J Nucl Med. 2021, 62:17-21. 10.2967/jnumed.120.256032

Gelhaus P: Robot decisions: on the importance of virtuous judgment in clinical decision making. J Eval Clin Pract. 2011, 17:883-7. 10.1111/j.1365-2753.2011.01720.x

Abdullah R, Fakieh B: Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020, 22:e17620. 10.2196/17620

Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, and King D: Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019, 17:195. 10.1186/s12916-019-1426-2

Hasani N, Farhadi F, Morris MA, et al.: Artificial intelligence in medical imaging and its impact on the rare disease community: threats, challenges and opportunities. PET Clin. 2022, 17:13-29. 10.1016/j.cpet.2021.09.009

Holland S, Burke W, Edwards K, and Goering S, Trinidad S: Achieving Justice in Genomic Translation: Rethinking the Pathway to Benefit. Oxford University Press, New York; 2011.

Burgart AM, Magnus D, Tabor HK, et al.: Ethical challenges confronted when providing nusinersen treatment for spinal muscular atrophy. JAMA Pediatr. 2018, 172:188-92. 10.1001/jamapediatrics.2017.4409

Chiruvella V, Guddati AK: Ethical issues in patient data ownership. Interact J Med Res. 2021, 10:e22269. 10.2196/22269

Sebastian AM, Peter D: Artificial intelligence in cancer research: trends, challenges and future directions. Life (Basel). 2022, 12:10.3390/life12121991

Kataoka H, Tanaka M, Kanamori M, et al. Expression profile of EFNB1, EFNB2, two ligands of EPHB2 in human gastric cancer.J Cancer Res Clin Oncol. 2002; 128(7):343–348.

Wang J, Li G, Ma H, et al. Differential expression of EphA7 receptor tyrosine kinase in gastric carcinoma. Hum Pathol. 2007; 38 (11):1649–1656.

Wang J, Dong Y, Sheng Z, et al. Loss of expression of EphB1 protein in gastric carcinoma associated with invasion and metastasis. Oncology. 2007; 73(3–4):238–245.

Jamal A. 2023 Novel Approaches in the Field of Cancer Medicine. Biological times, 2(12): 52-53

Wang X, Xu H, Wu Z, et al. Decreased expression of EphA5 is associated with Fuhrman nuclear grade and pathological tumour stage in ccRCC. Int J Exp Pathol. 2017; 98(1):34–39.

Downloads

ARTICLE Published HISTORY

Submitted Date: 2024-02-04
Accepted Date: 2024-02-04
Published Date: 2024-02-04