Using Artificial Intelligence for Medical Imaging & Diagnosis - UGC0725002

Introduction
The paper is based on researches on the topic of "Clinical Applications of Artificial intelligence in medical imaging and image processing" done by (Rafal Obuchowicz, Michael Strzelecki and Adam Piorkowsi published in the year of 14th May 2024 ). This paper covers the modalities MRI, CT, X-ray, mammography, and ultrasound. The authors are mainly summarizing the topics Image segmentation, Disease detection and diagnosis, Image preprocessing, Personalized treatment planning, Predictive analytics, Quality control and Monitoring. While the article includes relevant information on the clinical applications and also Medical Imaging it does not explore many topics on AI in the healthcare industry.


Summary
The articles review begin by exploring the wide area of the subtopics of Artificial intelligence and also highlight their primary uses of the topics Image segmentation, Disease detection and diagnosis, Image preprocessing, Personalized treatment planning, Predictive analytics, Quality control and Monitoring.
The review is mainly divided into two main parts they are a review of every single subtopic and a modality for each.The summarized works expands accross clinical applications, from detecting brain metastases using MRI to automating breast cancer detection via tomosynthesis and histopathology images.It also explores on the other topics such as Histological Analyses and Comparative studies. The paper concludes by emphasizing AI’s potential to go deeper on improving diagnostic precision and patient outcomes across various imaging techniques.

Critical Analysis

The strengths are tht the review offers a very informative overview of AI applications, effectively bridging up many imaging modalities. It demonstrates a structured classification of studies, making it easier for readers to follow developments by imaging type. It also deeply explore the the subtopics deeply and also explains the uses of them individually.Quantitative data such as accuracy rates give meaningful evidences of AI effectiveness on the topic.

The weaknesses are the paper functions largely as a descriptive catalog rather than a critical synthesis it lists studies but seldom compares or critiques their methodologies.Ethical and legal implications, such as data bias, transparency, or patient consent, are not sufficiently explored, despite being key challenges in AI healthcare adoption.The lack of discussion on data standardization and cross-institutional validation limits the paper’s practical relevance for clinical deployment.The paper also lacks the vast majority of the topics under the main topic of "Using Artificial Intelligence for Medical Imaging & Diagnosis". 

In my point of view, while the article succeeds in compiling valuable data, it doesn’t fully address the “so what” , why certain AI approaches outperform others, or how these can be realistically integrated into hospital workflows so considering all the strengths and weaknesses the article is is informative at the same time not descriptive enough on certain topics.The article is useful as a reference resource for understanding current AI techniques in medical imaging. It also contributes to the growing body of interdisciplinary research linking computer science and medicine. However, its academic tone and lack of critical interpretation make it less engaging for general readers or practitioners seeking actionable insights. The absence of future research directions, especially concerning model generalization, interpretability, and clinical validation, is another missed opportunity.

Conclusion
In summary, the article "Clinical Applications of Artificial intelligence in medical imaging and image processing" done by (Rafal Obuchowicz, Michael Strzelecki and Adam Piorkowsi published in the year of 14th May 2024 ), successfully present a broad and informative review of AI’s role in medical imaging, providing a clear snapshot of the field’s current capabilities. Yet, the review could be more descriptive and tht would benefit from deeper analytical discussion and greater attention to ethical, operational, and practical considerations. Despite these limitations, it stands as a valuable academic resource for understanding how artificial intelligence continues to revolutionize diagnostic imaging and patient care.

Reference
Article topic: "Clinical Applications of Artificial intelligence in medical imaging and image processing" 
Authors: Rafal Obuchowicz, Michael Strzelecki and Adam Piorkowsi published in the year of 14th May 2024