Machine Learning and Vision Systems in Precision Medical Detection: a systematic review

Approaches in artificial intelligence including machine learning and computer vision reshape how diagnoses occur in healthcare. They let automated tools examine visuals independently while producing forecasts. In this review we examine key advances spanning ۲۰۲۰ through ۲۰۲۵ and highlight their role in identifying diseases. Techniques like convolutional neural networks process scans for early identification in oncology neurology and cardiology. These tools boost sensitivity by ۲۰-۳۰ percent over traditional methods. Challenges include data biases algorithmic opacity and integration hurdles. Analyzing ۴۰ studies the review shows ML's success in glaucoma diagnosis with ۹۵ percent accuracy rates. CV excels in dermatology spotting skin lesions via mobile apps. Gaps persist in diverse datasets limiting generalizability. As a single-author contribution from a general practitioner this highlights translational benefits for routine screenings. Outcomes support hybrid systems where ML augments human expertise reducing errors. Ethical frameworks are vital to address privacy and equity. Future paths involve federated learning for collaborative models without data sharing. This work advocates for regulated adoption to enhance global diagnostics ensuring timely interventions. Overall ML and CV promise a paradigm shift toward proactive personalized medicine.

کلیدواژه ها:

Machine learning diagnostics ، computer vision medicine ، precision detection ، AI imaging ، health algorithms