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Artificial Intelligence in Health AI in acute stroke imaging

continuous medical education is vital for keeping pace Acknowledgments
with advancements in AI tools and techniques.
None.
The lack of historical records also limits diagnostic
accuracy. Integrating multimodal data, including clinical Funding
history and laboratory results, with stroke imaging is crucial None.
for prognostic analysis, allowing timely diagnosis, early
intervention, treatment guidance, and outcome monitoring. 78 Conflict of interest
Finally, regulatory compliance and the integration of The authors declare that they have no competing interests.
AI into clinical workflows are paramount. AI tools must
be rigorously validated and approved by regulatory bodies Author contributions
such as the Food and Drug Administration prior to their
deployment in clinical settings. Despite these challenges, Conceptualization: Arjun Kalyanpur
AI algorithms hold immense promise as transformative Formal analysis: Neetika Mathur
tools in stroke care. 13 Investigation: All authors
Methodology: Neetika Mathur
5. Conclusion Supervision: Arjun Kalyanpur
Visualization: Arjun Kalyanpur
Acute stroke is a time-sensitive clinical situation where Writing – original draft: Neetika Mathur
swift assessment and treatment are critical. The refinement Writing – review & editing: Arjun Kalyanpur
of guidelines and protocols, along with the implementation
of technologies that reduce time to treatment, will remain Ethics approval and consent to participate
central areas of focus in stroke care. The development and
integration of AI algorithms into clinical workflows can Not applicable.
detect subtle signs of stroke, quantify infarct size, assess Consent for publication
collateral status, predict patient outcomes, and guide
prognosis and post-stroke recovery planning. AI has Not applicable.
revolutionized stroke imaging by improving detection,
enabling synchronous communication, and enhancing Availability of data
triage, diagnosis, and prognosis assessment. Not applicable.
Emerging AI technologies should be leveraged with
transparency, supported by appropriate legislation and References
regulation, to enhance both clinical impact and the 1. Soun JE, Chow DS, Nagamine M, et al. Artificial intelligence
credibility of these algorithms. and acute stroke imaging. AJNR Am J Neuroradiol.
2021;42(1):2-11.
In conclusion, the integration of AI tools into the
teleradiology workflow can significantly address global doi: 10.3174/ajnr.A6883
workforce shortages in stroke care and tackle several 2. Behera DK, Rahut DB, Mishra S. Analyzing stroke burden
challenges, including ethical, legal, and societal implications. and risk factors in India using data from the Global Burden
of Disease Study. Sci Rep. 2024;14(1):22640.
Glossary
Term Definition doi: 10.1038/s41598-024-72551-4
Alberta Stroke Program A 10-point quantitative scoring system used 3. Cheng Y, Lin Y, Shi H, et al. Projections of the stroke
Early Computed to assess the extent of early ischemic changes burden at the global, regional, and national levels up to 2050
Tomography Score in the brain on computed tomography scans based on the global burden of disease study 2021. JAHA.
(ASPECTS) following an acute ischemic stroke 2024;13:e036142.
Deep learning A subset of machine learning that uses doi: 10.1161/JAHA.124.036142
multilayered neural networks, known as
deep neural networks, to simulate complex 4. Pandian JD, Padma Srivastava MV, Aaron S, et al. The
decision-making processes similar to those of burden, risk factors and unique etiologies of stroke in South-
the human brain East Asia Region (SEAR). he Lancet Reg Health Southeast
Artificial intelligence Companies that provide access to their Asia. 2023;17:100290.
vendors proprietary artificial intelligence models,
typically via Application Programming doi: 10.1016/j.lansea.2023.100290
Interfaces (APIs) 5. Jones SP, Baqai K, Clegg A, et al. Stroke in India: A systematic

Volume 2 Issue 4 (2025) 8 doi: 10.36922/AIH025140025