» Articles » PMID: 38706749

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward

Overview
Journal Int J Gen Med
Publisher Dove Medical Press
Specialty General Medicine
Date 2024 May 6
PMID 38706749
Authors
Affiliations
Soon will be listed here.
Abstract

This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI's capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI's capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

Citing Articles

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.

Fountzilas E, Pearce T, Baysal M, Chakraborty A, Tsimberidou A NPJ Digit Med. 2025; 8(1):75.

PMID: 39890986 PMC: 11785769. DOI: 10.1038/s41746-025-01471-y.


Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review.

Agudelo-Perez S, Botero-Rosas D, Rodriguez-Alvarado L, Espitia-Angel J, Raigoso-Diaz L Int Breastfeed J. 2024; 19(1):79.

PMID: 39639329 PMC: 11622664. DOI: 10.1186/s13006-024-00686-1.

References
1.
Murdoch B . Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021; 22(1):122. PMC: 8442400. DOI: 10.1186/s12910-021-00687-3. View

2.
Morley J, Floridi L . The Limits of Empowerment: How to Reframe the Role of mHealth Tools in the Healthcare Ecosystem. Sci Eng Ethics. 2019; 26(3):1159-1183. PMC: 7286867. DOI: 10.1007/s11948-019-00115-1. View

3.
Sauerbrei A, Kerasidou A, Lucivero F, Hallowell N . The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions. BMC Med Inform Decis Mak. 2023; 23(1):73. PMC: 10116477. DOI: 10.1186/s12911-023-02162-y. View

4.
Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L . Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Ann Intern Med. 2016; 165(11):753-760. DOI: 10.7326/M16-0961. View

5.
Aquino Y . Making decisions: Bias in artificial intelligence and data‑driven diagnostic tools. Aust J Gen Pract. 2023; 52(7):439-442. DOI: 10.31128/AJGP-12-22-6630. View