AI-Driven Early Disease Detection

Abstract

Artificial intelligence has transitioned from experimental technology to validated clinical tool for early disease detection across multiple medical domains, with deep learning architectures achieving high diagnostic accuracy—including 90% sensitivity and 98% specificity for diabetic retinopathy [1], 94.24% accuracy for lung and colon cancer detection [2], and pancreatic cancer identification up to three years before conventional diagnosis [3]. AI systems integrate diverse data sources including medical imaging (mammography, CT, MRI) [2, 4, 5], genomic and proteomic biomarkers [2, 6], electronic health records [3, 4, 7], and wearable device monitoring [4, 7] to detect pre-clinical disease states, providing lead times ranging from hours to days for acute conditions like sepsis [4] to years for neurodegenerative diseases with 10-20 year pre-clinical windows [8]. Multi-modal approaches combining radiomics, genomics, and clinical metadata [2, 9] demonstrate superior performance, with AI-supported breast cancer screening achieving 80.5% sensitivity compared to 73.8% for standard radiologist double-reading [2] and reducing false positives by 5.7% in U.S. populations [6].

However, clinical implementation lags behind technical performance due to persistent barriers including workflow integration challenges [2, 3], algorithmic bias requiring diverse training datasets [1, 7, 9], and the "black box" problem demanding explainable AI architectures to build provider trust [3, 7]. Regulatory frameworks evolved substantially by 2026, with approximately 70% of FDA-approved AI products concentrated in radiology [2] and the EU AI Act categorizing medical AI as "high-risk" requiring transparency and human oversight [2, 3, 7]. The establishment of Medicare reimbursement pathways [2] and successful deployment in both centralized hospital systems [2] and decentralized point-of-care settings [6, 7] indicate growing operational maturity. While AI consistently matches or exceeds human expert performance in controlled validation studies, sustained adoption depends critically on addressing data heterogeneity across institutions [5], mitigating alert fatigue from high-sensitivity systems [2, 5], resolving complex liability allocation questions [2], and ensuring equitable performance across demographic groups to avoid amplifying healthcare disparities [7, 9].

Flow Diagram

Figure: Study selection flow — Records from Elicit search (n = 50) → Papers screened using AI-Based Early Detection Focus, Study Design Quality, Diagnostic Performance Metrics, Clinical Validation, Diagnostic Application Focus, Study Type and Sample Size, AI Component and Clinical Relevance, Early Detection vs Acute Care (n = 50) → Papers included for extraction (n = 10); Papers screened out (n = 40; Other / below screening threshold: n = 40).

Paper search

We performed a semantic search across over 138 million academic papers from the Elicit search engine, which includes all of Semantic Scholar and OpenAlex.

We ran this query: "generate an research article on this

AI in Early Disease Detection I. Introduction • The Hook: A scenario where a patient's life is saved not by a doctor's intuition, but by an AI flagging a microscopic change in a routine scan years before symptoms emerge. • The Problem: Traditional "reactive" healthcare (treating symptoms) vs. the "proactive" vision. • Thesis: AI is no longer a future concept; in 2026, it is the cornerstone of early intervention, transforming chronic diseases from death sentences into manageable conditions.

II. The Technological Pillars (How it Works) • Medical Imaging: Explain how Deep Learning (DL) identifies patterns in X-rays, MRIs, and CT scans that are invisible to the human eye. o Example: 94% accuracy in detecting early-stage lung and breast cancer. • Multi-Omics Integration: The "Big Data" of biology. How AI combines DNA, RNA, and metabolic data to find "biomarkers." • Wearable & Continuous Monitoring: The role of smartwatches and implants in tracking real-time vitals to predict heart failure or stroke risks.

III. Key Clinical Frontiers (Case Studies) • Oncology: Liquid biopsies and AI-enhanced mammography (reducing false positives by 5.7%). • Neurological Disorders: Using AI to analyze voice patterns and hand tremors to catch Parkinson's or Alzheimer's years early. • Cardiovascular Health: AI predicting stroke risk using "Federated Learning" (keeping data private while the AI learns).

IV. The 2026 Landscape: Decentralized Diagnostics • The Shift: Moving testing from massive central labs to the "Point-of-Care" (your local clinic or even your home). • Hyper-Personalization: How AI adjusts "normal" ranges based on your specific genetic profile rather than a generic average.

V. The Human & Ethical Hurdles • The "Black Box" Problem: The need for Explainable AI (XAI)—doctors need to know why the AI flagged a patient to trust the result. • Algorithmic Bias: Ensuring datasets are diverse so the AI doesn't underperform on specific ethnicities or genders. • The Responsibility Gap: If the AI misses something, who is liable? (The developer, the hospital, or the doctor?) • Regulatory Status: Mention the 2026 implementation of the EU AI Act and FDA's high-risk AI frameworks.

VI. Conclusion • The Future Vision: A world where "prevention" isn't a moral argument but an automated, data-driven reality. • The Human Element: Reiterate that AI is a "co-pilot," not a replacement. It frees doctors to focus on the human side of care. • Final Thought: We are entering the era of "Medicine 3.0"—where the best hospital visit is the one that never has to happen."

The search returned 50 total results from Elicit.

We retrieved 50 papers most relevant to the query for screening.

Screening

We screened in sources based on their abstracts that met these criteria:

• AI-Based Early Detection Focus: Does this study involve AI-based diagnostic systems specifically designed for early disease detection (i.e., detecting diseases before clinical symptoms manifest)?
• Study Design Quality: Is this study a randomized controlled trial, cohort study, case-control study, systematic review, or meta-analysis?
• Diagnostic Performance Metrics: Does this study report diagnostic accuracy metrics such as sensitivity, specificity, AUC, or other quantifiable measures of AI system performance?
• Clinical Validation: Does this study include clinical validation or evidence of clinical applicability (i.e., is it NOT limited to purely technical algorithm development without clinical validation)?
• Diagnostic Application Focus: Is this study focused on diagnostic applications rather than treatment selection, drug discovery, therapeutic interventions, or disease monitoring in already diagnosed patients?
• Study Type and Sample Size: Is this study NOT a case report, editorial, opinion piece, or conference abstract, AND does it have a sample size of 50 or more participants?
• AI Component and Clinical Relevance: Does this study involve specific AI components for diagnostic purposes (i.e., is it NOT general health screening without AI or AI for administrative/non-diagnostic functions)?
• Early Detection vs Acute Care: Is this study focused on early/preventive detection rather than emergency or acute care diagnosis where disease is already clinically apparent?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

At abstract screening, the number of papers excluded for each primary reason was:

• Other / below screening threshold: n = 40

Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

• AI Technology Type: Extract the specific AI/machine learning approach used for early disease detection, including:
  • Algorithm type (deep learning, CNN, transformer, ensemble, etc.)
  • Model architecture details
  • Training methodology
  • Whether it's a single-modality or multi-modal system
  • Any novel AI techniques mentioned
• Target Disease Conditions: Extract all diseases or conditions targeted for early detection by the AI system, including:
  • Primary disease(s) being detected
  • Disease stage targeted (pre-clinical, early-stage, asymptomatic)
  • Specific subtypes or variants if mentioned
  • Time advantage provided (how much earlier than traditional methods)
• Data Input Sources: Extract all types of data inputs used by the AI system for early disease detection, including:
  • Medical imaging modalities (X-ray, MRI, CT, mammography, etc.)
  • Electronic health records data
  • Laboratory/biomarker data
  • Wearable device data
  • Genomic/molecular data
  • Patient-reported data
  • Any data preprocessing or integration methods
• Performance Metrics: Extract all reported performance measures for early disease detection, including:
  • Sensitivity and specificity values
  • AUC/ROC scores
  • Positive and negative predictive values
  • Accuracy percentages
  • Comparison to human clinicians or standard care
  • False positive/negative rates
  • Any improvements over traditional methods
• Study Design: Extract the validation approach and study methodology used to evaluate the AI system for early disease detection, including:
  • Study type (retrospective, prospective, RCT, etc.)
  • Sample size and population characteristics
  • Validation datasets used
  • Cross-validation or external validation approaches
  • Comparison groups or controls
  • Study duration and follow-up
• Clinical Implementation: Extract details about real-world clinical implementation of the AI system for early disease detection, including:
  • Healthcare setting (primary care, specialty clinics, hospitals)
  • Integration with existing workflows
  • Point-of-care vs. centralized implementation
  • Provider training requirements
  • Patient population served
  • Geographic deployment scope
• Implementation Barriers: Extract all challenges, limitations, or barriers related to implementing AI for early disease detection, including:
  • Technical challenges (data quality, interoperability)
  • Clinical workflow integration issues
  • Provider acceptance or trust issues
  • Cost or resource constraints
  • Algorithmic bias or equity concerns
  • Alert fatigue or false positive issues
  • Data privacy or security concerns
• Regulatory Status: Extract regulatory and approval information for the AI system used in early disease detection, including:
  • FDA clearance or approval status
  • CE marking or other regulatory approvals
  • Clinical trial registration details
  • Compliance with AI regulations (EU AI Act, etc.)
  • Quality assurance or monitoring requirements
  • Reimbursement status or coverage decisions

Results

Characteristics of Included Studies

The included studies represent a diverse body of evidence published between 2022 and 2026, encompassing both disease-specific reviews (retinal disease [1], dementia [8]) and broader multi-disease applications [4, 7, 10]. Seven of the ten studies provided full-text access, enabling more detailed extraction of technical specifications and implementation details. The reviews varied in methodological approach, including systematic reviews [1, 3], scoping reviews [4, 8], and narrative syntheses [7], reflecting the rapidly evolving nature of AI applications in early disease detection.

AI Technologies and Architectures

Deep learning architectures, particularly CNNs, emerged as the dominant AI technology across all disease domains [2, 5–7, 9]. The evolution from traditional CNNs to hybrid models incorporating Vision Transformers represents a significant architectural advancement, combining local feature precision with global context modeling [2]. Multi-modal AI systems integrating genomic, transcriptomic, proteomic, imaging, and clinical metadata [2] demonstrate the field's progression toward comprehensive diagnostic approaches. Novel techniques such as federated learning address privacy concerns by enabling collaborative training without sharing raw patient data [2], while generative models using diffusion techniques support data augmentation for rare diseases [2, 5].

Data Sources and Modalities

Medical imaging remained the most extensively utilized data source, spanning multiple modalities from traditional radiography to advanced pathology digitization [1–5]. The integration of multi-modal data represents a key trend, with systems combining imaging, genomic, and clinical metadata to enhance diagnostic accuracy [2, 9]. Electronic health records provided the infrastructure for opportunistic screening and continuous monitoring [4, 7], while wearable devices enabled real-time physiological tracking for cardiovascular conditions [4, 7]. Emerging data sources include speech pattern analysis for neurodegenerative diseases [3, 8] and electronic nose technology for non-invasive diabetes detection [3].

Performance Metrics and Clinical Validation

AI systems demonstrated consistently high performance across disease domains, with several models achieving or exceeding 90% accuracy thresholds. In diabetic retinopathy detection, deep learning algorithms reached 90% sensitivity and 98% specificity [1], while ophthalmology applications showed high clinical applicability [7]. Cancer detection models exhibited particularly strong performance, with the pancreatic cancer PANDA model achieving AUROCs between 0.986 and 0.996 [2], CNN-based pathology classification reaching 0.994 AUC [5], and the hybrid ViT-DCNN model demonstrating 94.24% accuracy for lung and colon cancer [2].

Direct comparisons with human clinicians revealed AI's competitive or superior performance in specific contexts. The MASAI study for breast cancer showed AI-supported screening achieved 80.5% sensitivity compared to 73.8% for standard double reading by radiologists [2]. When radiologists used AI as a support tool, their AUROC improved from 0.810 to 0.881 [2]. AI breast cancer screening models demonstrated an 11.5% higher AUROC than average radiologists [6] and reduced false positives by 5.7% in the US and 1.2% in the UK, while decreasing false negatives by 9.4% and 2.7% respectively [6]. For prostate cancer, AI models showed pooled sensitivity of 0.87, slightly higher than radiologists' 0.85 [2].

AI also reduced radiologist workload by 44.3% [6] and pathologist workload by 57.2% [9] in specific screening applications. The lung cancer risk prediction model outperformed Centers for Medicare & Medicaid Services (CMS) eligibility criteria [9], while AI-enhanced mammography triage identified low-priority scans with no cancers found [9]. Beyond single-disease applications, multi-cancer detection systems like CancerSEEK achieved 91% AUC in predicting malignancy, particularly for ovarian and liver cancers [9].

Disease-Specific Applications and Time Advantages

AI applications targeted early-stage and pre-clinical disease detection across multiple domains, with particularly notable time advantages in specific conditions. For pancreatic cancer, AI systems predicted onset up to three years before conventional diagnosis [3], while dementia detection focused on the 10-20 year pre-clinical window before significant cognitive decline [8]. Cardiovascular and infectious disease monitoring provided lead times of hours to days compared to standard clinical scores [4], enabling rapid intervention for conditions like sepsis and atrial fibrillation [4, 7].

Cancer remained the most extensively studied application domain, with AI systems demonstrating early detection capabilities across 19 different cancer types [3]. Specific implementations included mammography screening for breast cancer [2, 4], low-dose CT scanning for lung cancer [2, 6], and liquid biopsy analysis for multi-cancer detection [6]. Metabolic diseases, particularly diabetes, benefited from pre-clinical detection before symptom manifestation [3, 10], while neurodegenerative conditions utilized speech pattern analysis and blood-based biomarkers for non-invasive early screening [3, 8].

Clinical Implementation and Workflow Integration

AI systems have been implemented across diverse healthcare settings, from primary care clinics to specialized hospitals [2, 3, 5, 7, 9, 10]. Integration with existing clinical workflows varied by application, with mammography screening and radiology representing the most mature implementations [2, 6]. Picture Archiving and Communication Systems (PACS) integration enabled centralized deployment within hospitals [2], while point-of-care applications emerged through AI-enabled chatbot symptom checkers and remote monitoring platforms [6].

The deployment strategy balanced centralized and decentralized approaches. Centralized implementation dominated in settings requiring standardized data protocols, particularly where medical imaging heterogeneity posed validation challenges [5]. Conversely, federated learning architectures enabled decentralized data handling while preserving privacy [7], supporting applications in low-resource and geographically remote settings [7]. Provider training requirements emphasized AI literacy and operational competency [7], with implied needs for understanding model interpretability to build clinical trust [5].

Geographic deployment expanded globally [2, 3], with specific focus on the U.S. healthcare system for validation in the American care context [4]. Implementation strategies increasingly addressed healthcare inequities through technologies accessible in resource-limited environments [7], supported by modern connectivity infrastructure including 5G and Internet of Things integration [3].

Implementation Barriers and Challenges

Implementation barriers spanned technical, clinical, ethical, and regulatory domains. Data quality and heterogeneity emerged as fundamental technical challenges [3, 7, 10], with AI models requiring large volumes of high-quality, labeled data that proved time-consuming and costly to generate [9]. Data standardization across institutions remained problematic [3, 7], while model robustness issues included over-fitting and performance deterioration due to institutional heterogeneity [5].

Clinical workflow integration faced resistance from healthcare providers concerned about disruption to established practices [2] and the complexity of incorporating AI into existing systems [3]. Alert fatigue represented a recognized risk [2, 5], potentially diminishing the clinical utility of AI recommendations. Provider acceptance hinged critically on explainability and transparency [3, 7], with the "black box" nature of deep learning models impeding clinical trust [3, 7]. The lack of rigorous evaluation in many published studies [9] and the need for external validation [5, 9] further complicated evidence-based adoption.

Algorithmic bias and equity concerns appeared consistently across reviews [1–3, 6, 7, 9, 10], with particular emphasis on model bias regarding demographic characteristics like sex and ethnicity [9] and the risk of amplifying existing healthcare inequities [7]. The need for diverse and representative datasets to ensure generalizability across populations was repeatedly emphasized [1]. Data privacy and security posed significant barriers [1, 2, 6, 7, 9, 10], including concerns about privacy breaches [7], potential information leakage even in federated learning systems [2], and ongoing data security vulnerabilities [9].

Resource constraints manifested through expensive data curation processes [9], the high cost of specialist diagnostic tools [8], and the need for significant infrastructure and skilled personnel [3]. Regulatory and governance challenges included fragmented and evolving regulatory frameworks [7], complex liability allocation questions when AI errors occur [2], and the absence of clear guidelines for AI-based medical device approval [10].

Regulatory Landscape and Approval Status

The regulatory landscape for AI in early disease detection evolved rapidly between 2021 and 2026, with approximately 70% of FDA-approved AI products concentrated in radiology and radiation oncology [2]. The FDA demonstrated a favorable stance toward AI medical devices [5], with increasing device submissions predominantly for "narrow AI" applications focused on specific tasks [2]. Notable approvals included Paige Prostate in 2021 [6] and the first autonomous AI system for diabetic retinopathy screening [7]. However, generative models remained primarily in research phases due to validation concerns [2].

European regulatory frameworks diverged in approach and stringency. The EU Medical Device Regulation (EU MDR) mandated demonstrable clinical safety and transparency [7], while the EU AI Act (2024) established legally binding obligations categorizing radiology AI as "high-risk" [2, 7]. This classification required transparency in training data, risk management protocols, and mandatory human oversight [2, 3, 7]. The Act specifically mandated explainability features for medical AI decision-support systems [3], addressing the "black box" challenge inherent in deep learning architectures.

Quality assurance frameworks emerged as critical implementation requirements. The FDA's Good Machine Learning Practice (GMLP) principles emphasized traceability and human oversight [7], while publication standards like CONSORT-AI, SPIRIT-AI, and TRIPOD-AI aimed to improve study quality and model reporting [9]. Multiple reviews emphasized the need for standardization initiatives and quality assurance frameworks for future AI model adoption [3, 6].

Reimbursement mechanisms began formalizing, with Medicare and other payers establishing pathways for AI-enabled services [2]. The Hospital Outpatient Prospective Payment System (OPPS) 2026 rule specifically addressed reimbursement for AI-assisted cardiac analyses [2], signaling growing payer acceptance of AI as a compensable clinical tool.

Synthesis: Reconciling Performance Heterogeneity and Implementation Gaps

The reviewed evidence reveals a paradox: while AI systems consistently demonstrate high diagnostic accuracy in controlled validation studies, their real-world clinical adoption remains limited by implementation barriers that extend beyond technical performance. This heterogeneity in outcomes reflects distinct margins of applicability rather than contradictory findings.

Performance Context Dependencies: AI performance metrics varied substantially based on disease stage, data modality, and validation setting. In well-circumscribed imaging tasks with large training datasets—such as diabetic retinopathy screening (90% sensitivity, 98% specificity) [1] and pancreatic cancer detection (0.986-0.996 AUROC) [2]—AI achieved or exceeded human expert performance. However, performance deteriorated when models encountered data heterogeneity across institutions [5], suggesting that reported accuracies apply primarily to populations similar to training cohorts. The 3-7% improvement over traditional methods for dementia screening [8] versus the 11.5% AUROC advantage for breast cancer detection [6] reflects different baseline human performance levels and task complexity rather than inconsistent AI capabilities.

Study Quality Hierarchy: While numerous studies reported positive results, methodological rigor varied substantially. The prospective MASAI trial involving over 100,000 women [2] provided stronger evidence than retrospective analyses or reviews synthesizing heterogeneous literature [3, 5]. External validation remained limited, with many models evaluated only on single-institution datasets [5, 9], raising concerns about generalizability. The lack of rigorous evaluation frameworks [9] and the absence of true comparator models [5] in some studies suggest that reported performance may overestimate real-world effectiveness. This bias toward publication of positive results [6] necessitates cautious interpretation of aggregate performance metrics.

Implementation Mechanism Dependencies: The transition from algorithmic performance to clinical impact depends critically on workflow integration mechanisms. AI systems integrated into PACS for centralized radiology review [2] demonstrated different adoption patterns than point-of-care chatbot applications [6]. Alert fatigue emerged as a recognized implementation risk [2, 5], suggesting that high sensitivity systems may paradoxically reduce clinical utility if they generate excessive false alarms. The 44.3% workload reduction for radiologists [6] and 57.2% for pathologists [9] materialized only when AI served as a triage tool rather than requiring review of all cases—highlighting how deployment strategy mediates outcome measurement.

Temporal and Disease-Specific Factors: Time advantages varied by disease pathophysiology. The 10-20 year pre-clinical window for dementia [8] enabled fundamentally different early detection strategies than the hours-to-days lead time for sepsis [4]. Pancreatic cancer's 3-year early detection window [3] represented a major clinical advance precisely because conventional screening lacks effective biomarkers, whereas breast cancer AI's primary value lay in reducing false positives [6] in an already established screening program. These differing marginal utilities explain varying levels of clinical adoption enthusiasm across specialties.

Equity and Bias Considerations: Algorithmic bias emerged as a persistent concern across all reviewed studies [1–3, 6, 7, 9, 10], particularly regarding demographic characteristics like sex and ethnicity [9]. Models trained on non-representative datasets risk amplifying healthcare inequities [7], with performance degradation in underrepresented populations potentially exceeding the reported aggregate accuracy. The emphasis on diverse training data [1] and attention to structural racism [9] reflected recognition that technical performance metrics alone inadequately capture equitable healthcare delivery.

Regulatory and Economic Barriers: The 70% concentration of FDA approvals in radiology [2] revealed regulatory pathways better suited to imaging AI than to multi-modal or generative systems [2]. The EU's "high-risk" categorization requiring transparency and human oversight [2, 3, 7] created stricter requirements than FDA pathways, potentially explaining geographic variations in deployment. The emergence of Medicare reimbursement pathways [2] addressed a critical economic barrier, as clinical adoption historically lagged in the absence of payment mechanisms. The complex liability landscape [2] with unresolved questions about responsibility allocation when AI systems err further impeded implementation despite strong technical performance.

Explainability as a Mediating Factor: The repeated emphasis on explainable AI (XAI) [2, 3, 7] reflected recognition that black-box model architectures—despite superior accuracy—faced adoption resistance from clinicians requiring interpretable recommendations [3, 7]. The development of attention visualization, SHAP values, and saliency maps [2] represented attempts to bridge this trust gap. Models with built-in explainability features appeared more likely to achieve clinical integration [3], suggesting that transparency requirements may trade marginal performance gains for substantially improved adoption.

The evidence collectively indicates that AI in early disease detection has achieved technical maturity for well-defined imaging tasks with large training datasets, consistently matching or exceeding human expert performance in controlled settings. However, sustained clinical adoption depends less on further performance improvements than on addressing workflow integration, algorithmic bias mitigation, explainability enhancement, regulatory harmonization, and reimbursement pathway establishment. Future success requires shifting focus from pure accuracy optimization toward implementation science, with particular attention to the human-AI collaboration models that maximize clinical benefit while maintaining provider autonomy and patient trust.

References

1. Maryam Fatima, Praveen Pachauri, Wasim Akram, et al (2024) Enhancing retinal disease diagnosis through AI: Evaluating performance, ethical considerations, and clinical implementation. Informatics and Health. https://doi.org/10.1016/j.infoh.2024.05.003
2. Taib Ali, Imtiaz Ali, Ghulam Yasin (2026) Artificial Intelligence-Based Diagnostic Models for Early Detection of Cancer Using Medical Imaging. Physical Education, Health and Social Sciences. https://doi.org/10.63163/jpehss.v4i1.1129
3. Junyu Zhou, Sunmi Park, Sihan Dong, et al (2025) Artificial intelligence-driven transformative applications in disease diagnosis technology. Medical Review. https://doi.org/10.1515/mr-2024-0097
4. Benny Uhoranishema, P. Odufuwa, Kelvin Ebo Rabbles, et al (2025) Artificial Intelligence Applications for Early Disease Detection in U.S. Healthcare Systems. Journal of Advances in Medicine and Medical Research. https://doi.org/10.9734/jammr/2025/v37i126024
5. Daniyah Zehra Hussain, Amna Zaheer, Ahmed Akhtar (2024) Advancing cancer care with artificial intelligence: early detection initiatives. The Evidence. https://doi.org/10.61505/evidence.2024.2.4.91
6. N. Thaker, A. Dicker, Arturo Loaiza-Bonilla, et al (2024) The Role of Artificial Intelligence in Early Cancer Detection: Exploring Early Clinical Applications. AI in Precision Oncology. https://doi.org/10.1089/aipo.2023.0011
7. Achile Solomon Egbunu, A. Okedoye (2026) Harnessing Artificial Intelligence for Early Disease Detection: Opportunities and Challenges in Modern Healthcare. Journal of Computing Theories and Applications. https://doi.org/10.62411/jcta.15367
8. Renjie Li, Xinyi Wang, K. Lawler, et al (2022) Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions. Journal of Biomedical Informatics. https://doi.org/10.1016/j.jbi.2022.104030
9. B. Hunter, S. Hindocha, R. Lee (2022) The Role of Artificial Intelligence in Early Cancer Diagnosis. Cancers. https://doi.org/10.3390/cancers14061524
10. Jaswinder Singh, Gaurav Dhiman (2025) A Review on Predictive Analytics for Early Disease Detection in Neonatal Healthcare using Artificial Intelligence. Journal of Neonatal Surgery. https://doi.org/10.52783/jns.v14.2158