Ankita Tuteja
Papers
1
Total Citations
2
H-Index
1
About
Ankita Tuteja’s research lies at the intersection of machine learning and clinical healthcare, where she explores how computational methods can enhance medical decision-making and patient outcomes. Her most-cited work, “A Review of Machine Learning Approaches in Clinical Healthcare” (2021), provides a comprehensive synthesis of algorithmic applications in diagnostics, prognosis, and treatment planning, offering a critical lens on the challenges and opportunities in deploying AI within real-world clinical settings. While her citation count is still growing—a reflection of the emerging nature of her contributions—this review has already served as a foundational resource for researchers navigating the rapidly evolving landscape of healthcare AI. Tuteja’s work is notable for its clarity in bridging technical methodologies with practical clinical needs, making complex concepts accessible to both computer scientists and healthcare professionals. As the demand for trustworthy, interpretable AI in medicine intensifies, her contributions are poised to shape how future systems are designed, validated, and integrated. For students and researchers entering this field, Tuteja’s review offers a vital roadmap, highlighting both the promise and the pitfalls of machine learning in transforming clinical practice.
Research Focus
Key Achievements
Top Papers
- 1A Review of Machine Learning Approaches in Clinical Healthcare2 citations · 2021