Enhancing mental health diagnostics through deep learning-based image classification
IntroductionThe integration of artificial intelligence (AI) and machine learning technologies into healthcare, particularly for enhancing mental health diagnostics, represents a critical frontier in advancing patient care. Key challenges within this domain include data scarcity, model interpretabili...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1627617/full |
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| author | Lixin Zhang Ruotong Zeng |
| author_facet | Lixin Zhang Ruotong Zeng |
| author_sort | Lixin Zhang |
| collection | DOAJ |
| description | IntroductionThe integration of artificial intelligence (AI) and machine learning technologies into healthcare, particularly for enhancing mental health diagnostics, represents a critical frontier in advancing patient care. Key challenges within this domain include data scarcity, model interpretability, robustness under domain shifts, and trustworthy decision-making—issues pivotal to the context of mental health and cognitive neuroscience.MethodsWe propose a novel deep learning framework, MedIntelligenceNet, enhanced with Clinical-Informed Adaptation. MedIntelligenceNet integrates multi-modal data fusion, probabilistic uncertainty quantification, hierarchical feature abstraction, and adversarial domain adaptation into a unified model architecture. The Clinical-Informed Adaptation strategy employs structured clinical priors, symbolic reasoning, and domain alignment techniques to address interpretability and robustness concerns in healthcare AI.ResultsEmpirical evaluations conducted on multi-modal mental health datasets demonstrate that our framework achieves notable improvements in diagnostic accuracy, model calibration, and resilience to domain shifts, surpassing baseline deep learning methods.DiscussionThese results underscore the effectiveness of integrating clinical knowledge with advanced AI techniques. Our approach aligns with broader goals in healthcare AI: fostering more personalized, transparent, and reliable diagnostic systems for mental health. Ultimately, it supports the development of diagnostic tools that generalize better, quantify uncertainty more reliably, and align more closely with clinical reasoning. |
| format | Article |
| id | doaj-art-e710ba72368d4b399360fab114985bd3 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-e710ba72368d4b399360fab114985bd32025-08-20T03:18:27ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-08-011210.3389/fmed.2025.16276171627617Enhancing mental health diagnostics through deep learning-based image classificationLixin Zhang0Ruotong Zeng1Hebei University of Economics and Business, Shijiazhuang, ChinaGuangxi University, Nanning, ChinaIntroductionThe integration of artificial intelligence (AI) and machine learning technologies into healthcare, particularly for enhancing mental health diagnostics, represents a critical frontier in advancing patient care. Key challenges within this domain include data scarcity, model interpretability, robustness under domain shifts, and trustworthy decision-making—issues pivotal to the context of mental health and cognitive neuroscience.MethodsWe propose a novel deep learning framework, MedIntelligenceNet, enhanced with Clinical-Informed Adaptation. MedIntelligenceNet integrates multi-modal data fusion, probabilistic uncertainty quantification, hierarchical feature abstraction, and adversarial domain adaptation into a unified model architecture. The Clinical-Informed Adaptation strategy employs structured clinical priors, symbolic reasoning, and domain alignment techniques to address interpretability and robustness concerns in healthcare AI.ResultsEmpirical evaluations conducted on multi-modal mental health datasets demonstrate that our framework achieves notable improvements in diagnostic accuracy, model calibration, and resilience to domain shifts, surpassing baseline deep learning methods.DiscussionThese results underscore the effectiveness of integrating clinical knowledge with advanced AI techniques. Our approach aligns with broader goals in healthcare AI: fostering more personalized, transparent, and reliable diagnostic systems for mental health. Ultimately, it supports the development of diagnostic tools that generalize better, quantify uncertainty more reliably, and align more closely with clinical reasoning.https://www.frontiersin.org/articles/10.3389/fmed.2025.1627617/fullmental health diagnosticsdeep learningmulti-modal data fusionuncertainty quantificationclinical-informed adaptation |
| spellingShingle | Lixin Zhang Ruotong Zeng Enhancing mental health diagnostics through deep learning-based image classification Frontiers in Medicine mental health diagnostics deep learning multi-modal data fusion uncertainty quantification clinical-informed adaptation |
| title | Enhancing mental health diagnostics through deep learning-based image classification |
| title_full | Enhancing mental health diagnostics through deep learning-based image classification |
| title_fullStr | Enhancing mental health diagnostics through deep learning-based image classification |
| title_full_unstemmed | Enhancing mental health diagnostics through deep learning-based image classification |
| title_short | Enhancing mental health diagnostics through deep learning-based image classification |
| title_sort | enhancing mental health diagnostics through deep learning based image classification |
| topic | mental health diagnostics deep learning multi-modal data fusion uncertainty quantification clinical-informed adaptation |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1627617/full |
| work_keys_str_mv | AT lixinzhang enhancingmentalhealthdiagnosticsthroughdeeplearningbasedimageclassification AT ruotongzeng enhancingmentalhealthdiagnosticsthroughdeeplearningbasedimageclassification |