Editorial

Volume 1, 2025, e4

Artificial Intelligence in Clinical Medicine: A Timely Imperative

Artificial intelligence (AI) is rapidly reshaping the landscape of clinical medicine. From diagnostic imaging to patient triage, AI-driven tools are transitioning from research prototypes to integral components of healthcare delivery [1, 2]. Yet, as these technologies permeate clinical settings, there exists a conspicuous gap: the absence of a dedicated, peer-reviewed platform to critically assess, validate, and guide the integration of AI into clinical practice. The launch of the journal of AI in Clinical Medicine (AICM) seeks to address this void, providing a forum for interdisciplinary dialogue and evidence-based evaluation.

The integration of AI into healthcare is not merely a technological evolution but a paradigm shift. AI algorithms now assist in interpreting radiological images with accuracy comparable to, or surpassing, human experts [3, 4]. Predictive models forecast patient deterioration, enabling preemptive interventions [5]. Natural language processing tools streamline documentation, reducing clinician burnout [6]. Despite these advancements, the deployment of AI in clinical settings often outpaces the establishment of rigorous validation protocols and ethical guidelines [7, 8].

Existing medical journals, while occasionally publishing AI-related studies, lack the concentrated focus necessary to address the multifaceted challenges posed by AI integration. AICM aims to fill this niche, offering a centralized repository for high-quality research, implementation studies, policy analyses, and ethical discussions pertinent to AI in clinical medicine.

Several pressing issues underscore the necessity of AICM.

Many AI models demonstrate impressive performance in controlled environments but falter in real-world clinical settings [9]. AICM will prioritize studies that emphasize external validation, reproducibility, and generalizability across diverse populations and healthcare systems [10].

The deployment of AI raises questions about accountability, patient consent, data privacy, and algorithmic bias [11, 12]. By fostering interdisciplinary discourse, AICM will explore frameworks to navigate these complex ethical landscapes [13].

Successful AI implementation requires seamless integration into existing clinical workflows. AICM will highlight studies that address practical challenges, user interface design, and clinician engagement strategies [14, 15].

As regulatory bodies grapple with the oversight of AI tools, AICM will serve as a conduit between researchers, clinicians, and policymakers, informing evidence-based regulatory frameworks [16, 17].

The establishment of AICM is poised to catalyze several positive developments.

By disseminating validated AI tools and best practices, AICM can contribute to improved diagnostic accuracy, personalized treatment plans, and optimized resource allocation [18, 19].

AICM will encourage research that examines the impact of AI on health disparities, ensuring that technological advancements do not exacerbate existing inequities [20].

As an open-access journal, AICM will facilitate international collaboration, enabling knowledge sharing across borders and fostering a global community dedicated to the responsible integration of AI in medicine [21].

AICM will serve as an educational platform, equipping clinicians, researchers, and students with the knowledge to critically appraise and implement AI tools [22, 23].

The intersection of AI and clinical medicine presents unprecedented opportunities and challenges. The launch of the Journal of AI in Clinical Medicine represents a proactive step toward harnessing AI’s potential while safeguarding patient welfare and ethical standards. By providing a dedicated platform for rigorous scholarship and interdisciplinary dialogue, AICM aspires to guide the responsible evolution of AI in healthcare.

Acknowledgments

None to declare.

Financial Disclosure

None to declare.

Conflict of Interest

None to declare.

Author Contributions

Licun Wu and Hong Chang jointly contributed to the conceptualization of the research idea and study design. Both authors wrote the original draft of the manuscript and participated in reviewing, editing, and finalizing the manuscript.

Data Availability

The authors declare that data supporting the findings of this study are available within the article.

1. 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(7639):115-118.
   doi pubmed
2. Rajpurkar P, et al. CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv. 2017.
3. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
   doi pubmed
4. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-961.
   doi pubmed
5. Tomasev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116-119.
   doi pubmed
6. Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, Forshee R, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. 2017;73:14-29.
   doi pubmed
7. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
   doi pubmed
8. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195.
   doi pubmed
9. Heaven WD. Why AI still hasn’t revolutionized medicine. MIT Tech Rev. 2020. https://www.technologyreview.com/2020/10/05/1009152/ai-medicine-healthcare/.
10. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, Kalloo A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836-1842.
    doi pubmed
11. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453.
    doi pubmed
12. Yu KH, Kohane IS. Framing the challenges of artificial intelligence in medicine. BMJ Qual Saf. 2019;28(3):238-241.
    doi pubmed
13. WHO/FDA. Regulatory Considerations for AI in Health. 2024. https://www.who.int/publications.
14. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243.
    doi pubmed
15. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98.
    doi pubmed
16. U.S. Food & Drug Administration. Artificial intelligence and machine learning in software as a medical device. https://www.fda.gov/.
17. European Commission. Proposal for a regulation laying down harmonised rules on artificial intelligence. 2021.
18. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics. 2017;37(2):505-515.
    doi pubmed
19. Chen JH, Asch SM. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507-2509.
    doi pubmed
20. Adamson AS, et al. Bias in dermatology AI. Nat Med. 2023;29(5):903-909.
    doi
21. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-1318.
    doi pubmed
22. Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA. 2016;316(22):2353-2354.
    doi pubmed
23. Zhang J, et al. Deep multimodal learning in medicine. arXiv preprint. 2025.
    doi

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial 4.0 International License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

AI in Clinical Medicine is published by Elmer Press Inc.