{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T08:47:09Z","timestamp":1782463629234,"version":"3.54.5"},"reference-count":71,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Neurodegenerative diseases (NDDs) are becoming a major worldwide issue, especially for the elderly because they are incurable and permanent. It is extremely difficult to provide any medication to people suffering from NDDs. Comprehending essential processes of NDDs are essential to establishing alternative strategies that can increase survival rates in patients. This review is an effort to provide insight into NDDs, innovative therapeutic methods along with their clinical significances and thus provide opportunities for improving therapies for NDDs in the near future.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This work aims at a thorough, methodical and critical evaluation of Machine Learning (ML)\/Deep Learning (DL) efforts in diagnostics of NDDs, as well as their potential for use in pharmacological, clinical and research settings. An additional objective is to discuss problems and limitations of models, with an eye on furthering research directions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The databases of Elsevier, Sage, Wiley, Springer Link, Emerald Insights and Research Gate were searched for useful assessments of NDDs using ML\/DL approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Numerous potential diagnostic ML\/DL models have been suggested and effectively applied to detect NDDs. Biological, neuroimaging and key clinical characteristics in patients have been used to evaluate prognostic models. Additionally, these models provide options for patient categorization, facilitate earlier detections, enhanced diagnosis and better disease outcome predictions for personalized therapies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This work points out the rising burden of NDDs on the global elderly while highlighting the significances of ML\/DL technological advancements with their limitations for NDDs in diagnostics, identification of effective disease bio-markers and management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj-cs.3860","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T08:04:21Z","timestamp":1782461061000},"page":"e3860","source":"Crossref","is-referenced-by-count":0,"title":["Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative 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