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@article{189106,
        author = {Asmita Srivastava and Bhoomika Bhojwani and Charu Rajput and Ms. Jyoti Gaur},
        title = {AI Based Early Detection of Parkinson’s Disease},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7922-7928},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189106},
        abstract = {Parkinson’s Disease sticks around for the long haul, and spotting it early is tough. The first signs? They’re subtle, they creep in slowly, and honestly, they look different for everyone. Right now, doctors mostly rely on what they see and what they know. That means early diagnosis often comes late or with a lot of uncertainty. But here’s what’s exciting: with big leaps in Artificial Intelligence and Machine Learning, researchers can now pick up on early clues hidden in all sorts of places how someone speaks, the way they walk, how their handwriting changes, what shows up in brain scans, even data from wearables or blood tests. In this review, I dig into the latest AI strategies for catching Parkinson’s early. That means everything from single-source methods to more complex ones that blend different types of data. Deep learning models are front and center think CNNs, LSTMs, Transformers, Graph Neural Networks, and the newer self-supervised learning tricks. I’ve pulled together insights from recent work to compare the most promising techniques, what kinds of data people are using, how well these systems actually perform, and what tools help explain their decisions. There are some real challenges, though. Combining all those data types isn’t easy. Missing or messy data is a headache. Bias in datasets can mess up results, and it’s a whole other story trying to make these models work outside the lab. Plus, most clinics still don’t use explainable AI in their day-to-day decisions. By calling out these issues, this review tries to map out where we go from here how we can build smarter, more understandable, and actually useful AI tools to help doctors spot Parkinson’s early and keep tabs on patients over time. The idea is pretty simple: give researchers and healthcare pros a down-to- earth look at where things stand, and lay out the next steps for building better, more reliable ways to diagnose Parkinson’s.},
        keywords = {Parkinson’s Disease, Explainable AI (XAI), Grad-CAM, Artificial Intelligence},
        month = {December},
        }