AI in Early Diagnosis of Chronic Diseases Article Swipe
Saavan P. Asodaria
,
Payal Shah
,
Nirav R. Shah
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.55041/ijsrem51835
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.55041/ijsrem51835
- Chronic diseases (non-communicable diseases) such as diabetes, cardiovascular disease, cancer, and neurological disorders impose a tremendous global health burden, accounting for over 40 million deaths annually (≈71% of all deaths). Early diagnosis is critical to improving outcomes and reducing healthcare costs, yet many chronic conditions manifest subtly and are detected only at advanced stages. Artificial Intelligence (AI) – encompassing machine learning (ML), deep learning (DL), and data analytics – offers powerful tools for analyzing large-scale patient data (e.g. electronic health records, imaging, and wearable sensors) to detect disease signatures before clinical symptoms appear. In this review, we survey recent literature on AI-assisted early diagnosis of multiple chronic diseases. We outline key AI methods (e.g. neural networks, ensemble learning, natural language processing) and discuss real-world case studies: for instance, deep learning on retinal images can diagnose diabetes complications, convolutional networks on mammograms can detect early-stage breast cancer, and ML models on ECG or wearable data can identify asymptomatic atrial fibrillation. We present examples of open datasets (e.g. ADNI, MIMIC-III, ECG databases) and illustrate how AI models trained on these can predict disease onset with high accuracy. We also address ethical and technical challenges – data privacy, algorithmic bias, interpretability, and regulatory issues – that arise in AI-driven diagnostics. Our key findings are that AI approaches consistently improve early detection accuracy across diseases, but require careful validation and ethical oversight. Finally, we discuss future directions, predicting that AI will increasingly enable personalized, proactive chronic care, contingent on solving data governance and explainability challenges.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.55041/ijsrem51835
- https://ijsrem.com/download/ai-in-early-diagnosis-of-chronic-diseases/?wpdmdl=58027&refresh=68a7b957cfce21755822423
- OA Status
- bronze
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413379073
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413379073Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.55041/ijsrem51835Digital Object Identifier
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AI in Early Diagnosis of Chronic DiseasesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-15Full publication date if available
- Authors
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Saavan P. Asodaria, Payal Shah, Nirav R. ShahList of authors in order
- Landing page
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https://doi.org/10.55041/ijsrem51835Publisher landing page
- PDF URL
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https://ijsrem.com/download/ai-in-early-diagnosis-of-chronic-diseases/?wpdmdl=58027&refresh=68a7b957cfce21755822423Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://ijsrem.com/download/ai-in-early-diagnosis-of-chronic-diseases/?wpdmdl=58027&refresh=68a7b957cfce21755822423Direct OA link when available
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Interpretability, Artificial intelligence, Deep learning, Computer science, Machine learning, Data science, Convolutional neural network, Medicine, Big data, Data miningTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.literature | 101 |
| abstract_inverted_index.mammograms | 142 |
| abstract_inverted_index.oversight. | 229 |
| abstract_inverted_index.predicting | 235 |
| abstract_inverted_index.real-world | 125 |
| abstract_inverted_index.regulatory | 201 |
| abstract_inverted_index.signatures | 90 |
| abstract_inverted_index.tremendous | 17 |
| abstract_inverted_index.validation | 226 |
| abstract_inverted_index.AI-assisted | 103 |
| abstract_inverted_index.algorithmic | 197 |
| abstract_inverted_index.challenges. | 252 |
| abstract_inverted_index.directions, | 234 |
| abstract_inverted_index.early-stage | 145 |
| abstract_inverted_index.large-scale | 76 |
| abstract_inverted_index.processing) | 122 |
| abstract_inverted_index.Intelligence | 57 |
| abstract_inverted_index.asymptomatic | 158 |
| abstract_inverted_index.consistently | 216 |
| abstract_inverted_index.diagnostics. | 208 |
| abstract_inverted_index.encompassing | 60 |
| abstract_inverted_index.increasingly | 239 |
| abstract_inverted_index.neurological | 13 |
| abstract_inverted_index.convolutional | 139 |
| abstract_inverted_index.fibrillation. | 160 |
| abstract_inverted_index.personalized, | 241 |
| abstract_inverted_index.cardiovascular | 9 |
| abstract_inverted_index.complications, | 138 |
| abstract_inverted_index.explainability | 251 |
| abstract_inverted_index.(non-communicable | 4 |
| abstract_inverted_index.interpretability, | 199 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.47762001 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |