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AI in Healthcare

AI in Healthcare: Benefits, Challenges and Future

A practical look at adoption opportunities and real risks.

Dr. Sarah AhmedDr. Sarah Ahmed
Apr 28, 2026
7 min read
1.1K views
Dr. Sarah Ahmed

Dr. Sarah Ahmed

Associate Professor, Department of Biomedical Engineering

University of Toronto, Canada

Dr. Sarah Ahmed's research focuses on medical image analysis, machine learning applications in healthcare, and computer vision.
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Abstract

This article introduces AI adoption themes in modern healthcare.

1. Introduction

Healthcare systems are adopting AI across diagnosis, operations, and triage.

2. Operational Impact

AI can reduce turnaround time, but governance and validation remain critical.

3. Adoption Risks

Bias, overreliance, and weak integration can reduce trust and practical usefulness.
  • Workflow mismatch
  • Model drift
  • Regulatory uncertainty

4. Future Directions

Safer, explainable, and auditable AI systems.

5. Conclusion

Benefits are meaningful when models remain clinician-centered.

References

  1. AI reference 1
  2. AI reference 2
  3. AI reference 3
View on PubMedDOI: 10.1000/example2Download PDF
Tags:AIHealthcareEthics
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