Advancing healthcare with AI: designing frameworks for diagnostics, personalized treatment, and enhanced efficiency

Segun Akinola; Segun Akinola

doi:10.3934/medsci.2024019

AIMS Medical Science

2024, Volume 11, Issue 3: 248-264. doi: 10.3934/medsci.2024019

Previous Article Next Article

Review Special Issues

Advancing healthcare with AI: designing frameworks for diagnostics, personalized treatment, and enhanced efficiency

Segun Akinola ^,

Johannesburg Business School, University of Johannesburg, 2092, South Africa

Received: 12 March 2024 Revised: 31 July 2024 Accepted: 05 August 2024 Published: 20 August 2024

The integration of artificial intelligence (AI) into healthcare has ushered in transformative advancements. In diagnostics, AI has revolutionized precision by excelling in radiology, pathology, and novel approaches like liquid biopsy analysis. Personalized treatment plans benefit from AI's ability to tailor interventions, minimizing risks and maximizing therapeutic efficacy. Administrative efficiency sees notable improvements as AI automates tasks, optimizes resource allocation, and addresses scalability challenges. Ethical considerations, encompassing data privacy, algorithmic bias, and transparency, are crucial for responsible AI adoption. Guided by the fair principles, stakeholders can promote fairness, accountability, and transparency. Findings in this paper reveal AI's rapid advancements in healthcare enhance diagnostics, surgery, and patient care, yet reliance on its accuracy requires caution. The future of AI in healthcare relies on ongoing collaboration, research, and adherence to ethical frameworks, promising a patient-centered and equitable healthcare landscape.
- artificial intelligence,
- diagnostics,
- early detection,
- risk assessment,
- health research
Citation: Segun Akinola. Advancing healthcare with AI: designing frameworks for diagnostics, personalized treatment, and enhanced efficiency[J]. AIMS Medical Science, 2024, 11(3): 248-264. doi: 10.3934/medsci.2024019

Related Papers:

Abstract

The integration of artificial intelligence (AI) into healthcare has ushered in transformative advancements. In diagnostics, AI has revolutionized precision by excelling in radiology, pathology, and novel approaches like liquid biopsy analysis. Personalized treatment plans benefit from AI's ability to tailor interventions, minimizing risks and maximizing therapeutic efficacy. Administrative efficiency sees notable improvements as AI automates tasks, optimizes resource allocation, and addresses scalability challenges. Ethical considerations, encompassing data privacy, algorithmic bias, and transparency, are crucial for responsible AI adoption. Guided by the fair principles, stakeholders can promote fairness, accountability, and transparency. Findings in this paper reveal AI's rapid advancements in healthcare enhance diagnostics, surgery, and patient care, yet reliance on its accuracy requires caution. The future of AI in healthcare relies on ongoing collaboration, research, and adherence to ethical frameworks, promising a patient-centered and equitable healthcare landscape.

Conflict of interest

The author declares no conflicts of interest.

References

[1]	Qayyum MU, Sherani AMK, Khan M, et al. (2023) Revolutionizing healthcare: the transformative impact of artificial intelligence in medicine. B Inform 1: 71-83.
[2]	Gill AY, Saeed A, Rasool S, et al. (2023) Revolutionizing healthcare: how machine learning is transforming patient diagnoses-a comprehensive review of AI's impact on medical diagnosis. J World Sci 2: 1638-1652. https://doi.org/10.58344/jws.v2i10.449
[3]	Panayides AS, Amini A, Filipovic ND, et al. (2020) AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform 24: 1837-1857. https://doi.org/10.1109/JBHI.2020.2991043
[4]	Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20: e262-e273. https://doi.org/10.1016/S1470-2045(19)30149-4
[5]	Grant MJ, Booth A (2009) A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Libr J 26: 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
[6]	Jayaraman PP, Forkan ARM, Morshed A, et al. (2020) Healthcare 4.0: a review of frontiers in digital health. Wires Data Min Knowl 10: e1350. https://doi.org/10.1002/widm.1350
[7]	Ali M (2023) A comprehensive review of AI's impact on healthcare: revolutionizing diagnostics and patient care. Bullet Jurnal Multidisiplin Ilmu 2: 1163-1173.
[8]	Najjar R (2024) Digital frontiers in healthcare: integrating mHealth, AI, and radiology for future medical diagnostics, In: Heston, T.F., Doarn, C.E. Editors. A Comprehensive Overview of Telemedicine. London: IntechOpen Limited. https://doi.org/10.5772/intechopen.114142
[9]	Feretzakis G, Juliebø-Jones P, Tsaturyan A, et al. (2024) Emerging trends in AI and radiomics for bladder, kidney, and prostate cancer: a critical review. Cancers 16: 810. https://doi.org/10.3390/cancers16040810
[10]	Dlamini Z, Francies FZ, Hull R, et al. (2020) Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J 18: 2300-2311. https://doi.org/10.1016/j.csbj.2020.08.019
[11]	Cucchiara F, Petrini I, Romei C, et al. (2021) Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 169: 105643. https://doi.org/10.1016/j.phrs.2021.105643
[12]	Ginghina O, Hudita A, Zamfir M, et al. (2022) Liquid biopsy and artificial intelligence as tools to detect signatures of colorectal malignancies: a modern approach in patient's stratification. Front Oncol 12: 856575. https://doi.org/10.3389/fonc.2022.856575
[13]	Stenzinger A, Alber M, Allgäuer M, et al. (2022) Artificial intelligence and pathology: from principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 84: 129-143. https://doi.org/10.1016/j.semcancer.2021.02.011
[14]	Aminizadeh S, Heidari A, Dehghan M, et al. (2024) Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artif Intell Med 149: 102779. https://doi.org/10.1016/j.artmed.2024.102779
[15]	Pasrija P, Jha P, Upadhyaya P, et al. (2022) Machine learning and artificial intelligence: a paradigm shift in big data-driven drug design and discovery. Curr Top Med Chem 22: 1692-1727. https://doi.org/10.2174/1568026622666220701091339
[16]	Akinola S, Telukdarie A (2023) Sustainable digital transformation in healthcare: advancing a digital vascular health innovation solution. Sustainability 15: 10417. https://doi.org/10.3390/su151310417
[17]	Hassija V, Chamola V, Mahapatra A, et al. (2024) Interpreting black-box models: a review on explainable artificial intelligence. Cogn Comput 16: 45-74. https://doi.org/10.1007/s12559-023-10179-8
[18]	Schwartz R, Vassilev A, Greene K, et al. (2022) Towards a standard for identifying and managing bias in artificial intelligence. NIST Special Publication. https://doi.org/10.6028/NIST.SP.1270
[19]	Rajkomar A, Hardt M, Howell MD, et al. (2018) Ensuring fairness in machine learning to advance health equity. Ann Intern Med 169: 866-872. https://doi.org/10.7326/M18-1990
[20]	Ueda D, Kakinuma T, Fujita S, et al. (2024) Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 42: 3-15. https://doi.org/10.1007/s11604-023-01474-3
[21]	Aminizadeh S, Heidari A, Toumaj S, et al. (2023) The applications of machine learning techniques in medical data processing based on distributed computing and the internet of things. Comput Methods Programs Biomed 241: 107745. https://doi.org/10.1016/j.cmpb.2023.107745
[22]	Sahu M, Gupta R, Ambasta RK, et al. (2022) Artificial intelligence and machine learning in precision medicine: a paradigm shift in big data analysis. Prog Mol Biol Transl Sci 190: 57-100. https://doi.org/10.1016.pmbts.2022.03.002
[23]	Schork NJ (2019) Artificial intelligence and personalized medicine, In: Von Hoff, D., Han, H. Editors. Precision medicine in Cancer therapy. 1 Ed., Switzerland: Springer, Cham, 265-283. https://doi.org/10.1007/978-3-030-16391-4_11
[24]	Wang H, Xiong R, Lai L (2023) Rational drug design targeting intrinsically disordered proteins. Wires Comput Mol Sci 13: p.e1685. https://doi.org/10.1002/wcms.1685
[25]	Pun FW, Ozerov IV, Zhavoronkov A (2023) AI-powered therapeutic target discovery. Trends Pharmacol Sci 44: 561-572. https://doi.org/10.1016/j.tips.2023.06.010
[26]	Chen Y, Esmaeilzadeh P (2024) Generative AI in medical practice: in-depth exploration of privacy and security challenges. J Med Internet Res 26: e53008. https://doi.org/10.2196/53008
[27]	Lainjo B (2024) A meta-study on optimizing healthcare performance with artificial intelligence and machine learning. J Autonom Intell 7: 1535. https://doi.org/10.32629/jai.v7i5.1535
[28]	Tao WJ, Zeng Z, Dang HX, et al. (2020) Towards universal health coverage: lessons from 10 years of healthcare reform in China. BMJ Glob Health 5: e002086. https://doi.org/10.1136/bmjgh-2019-002086
[29]	Haleem A, Javaid M, Singh RP, et al. (2022) Medical 4.0 technologies for healthcare: features, capabilities, and applications. Int Things Cyber Physic Syst 2: 12-30. https://doi.org/10.1016/j.iotcps.2022.04.001
[30]	Wamba-Taguimdje SL, Wamba SF, Kamdjoug JRK, et al. (2020) Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Bus Process Manag J 26: 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
[31]	Chowdhury S, Dey P, Joel-Edgar S, et al. (2023) Unlocking the value of artificial intelligence in human resource management through AI capability framework. Hum Resour Manag Rev 33: 100899. https://doi.org/10.1016/j.hrmr.2022.100899
[32]	Akindote OJ, Adegbite AO, Omotosho A, et al. (2024) Evaluating the effectiveness of it project management in healthcare digitalization: a review. Int Med Sci Res J 4: 37-50. https://doi.org/10.51594/imsrj.v4i1.698
[33]	Rodrigues JJPC (2009) Health information systems: concepts, methodologies, tools, and applications: concepts, methodologies, tools, and applications. Igi Global. https://doi.org/10.4018/978-1-60566-988-5
[34]	World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. Available from: https://www.who.int/publications-detail-redirect/9789240029200
[35]	Carter SM, Rogers W, Win KT, et al. (2020) The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast 49: 25-32. https://doi.org/10.1016/j.breast.2019.10.001
[36]	Smith H (2021) Clinical AI: opacity, accountability, responsibility and liability. AI Soc 36: 535-545. https://doi.org/10.1007/s00146-020-01019-6
[37]	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. https://doi.org/10.1093/jamia/ocz192
[38]	Williamson SM, Prybutok V (2024) Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci 14: 675. https://doi.org/10.3390/app14020675
[39]	Ali A, Al-Rimy BAS, Tin TT, et al. (2023) Empowering precision medicine: unlocking revolutionary insights through blockchain-enabled federated learning and electronic medical records. Sensors 23: 7476. https://doi.org/10.3390/s23177476
[40]	Floridi L, Cowls J, Beltrametti M, et al. (2018) AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds Mach 28: 689-707. https://doi.org/10.1007/s11023-018-9482-5
[41]	Bhidayasiri R, Goetz CG (2024) Embracing the promise of artificial intelligence to improve patient care in movement disorders, In: Bhidayasiri, R., Maetzler, W. Editors. Handbook of Digital Technologies in Movement Disorders. 1 Ed., Massachusetts: Academic Press, 11-23. https://doi.org/10.1016/B978-0-323-99494-1.00015-0
[42]	Kalmady SV, Paul AK, Narayanaswamy JC, et al. (2022) Prediction of obsessive-compulsive disorder: importance of neurobiology-aided feature design and cross-diagnosis transfer learning. Biol Psychiatry Cogn Neurosci Neuroimaging 7: 735-746. https://doi.org/10.1016/j.bpsc.2021.12.003
[43]	Harerimana G, Jang B, Kim JW, et al. (2018) Health big data analytics: a technology survey. IEEE Access 6: 65661-65678. https://doi.org/10.1109/ACCESS.2018.2878254
[44]	Morley J, Floridi L (2020) An ethically mindful approach to AI for health care. Lancet 395: 254-255. https://doi.org/10.1016/S0140-6736(19)32975-7
[45]	Morley J, Floridi L (2021) How to design a governable digital health ecosystem, In: Cowls, J., Morley, J. Editors. The 2020 Yearbook of the Digital Ethics Lab. 1 Ed., Netherland: Springer, Cham, 69-88. https://doi.org/10.1007/978-3-030-80083-3_8
[46]	Cleret de Langavant L, Bayen E, Yaffe K (2018) Unsupervised machine learning to identify high likelihood of dementia in population-based surveys: development and validation study. J Med Internet Res 20: e10493. https://doi.org/10.2196/10493
[47]	Moscoso A, Silva-Rodríguez J, Aldrey JM, et al. (2019) Prediction of Alzheimer's disease dementia with MRI beyond the short-term: implications for the design of predictive models. Neuroimage Clin 23: 101837. https://doi.org/10.1016/j.nicl.2019.101837
[48]	López-Martínez F, Núñez-Valdez ER, Gomez JL, et al. (2019) A neural network approach to predict early neonatal sepsis. Comput Electr Eng 76: 379-388. https://doi.org/10.1016/j.compeleceng.2019.04.015
[49]	Morley J, Machado C, Burr C, et al. (2019) The debate on the ethics of AI in health care: a reconstruction and critical review. Available at SSRN 3486518. https://doi.org/10.2139/ssrn.3486518
[50]	Morley J, Machado CCV, Burr C, et al. (2021) The ethics of AI in health care: a mapping, In: Floridi, L., Editor. Ethics, Governance, and Policies in Artificial Intelligence. 1 Ed., Netherland: Springer, Cham, 313-346. https://doi.org/10.1007/978-3-030-81907-1_18
[51]	Karimian G, Petelos E, Evers SMAA (2022) The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI Ethics 2: 539-551. https://doi.org/10.1007/s43681-021-00131-7

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)