This comprehensive review examines the transformative potential of Machine Learning (ML) and Deep Learning (DL) technologies in revolutionizing disease diagnosis and prediction across healthcare. Synthesizing research from 2015 to 2024, the study analyzes AI applications across sixteen diverse diseases including cardiovascular conditions, brain tumors, diabetes, Alzheimer's, Parkinson's, and various cancers. The methodology involves systematic analysis of various data types such as Electronic Health Records (EHRs), medical imaging, genomic datasets, and physiological signals, employing advanced models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and ensemble learning approaches. Results demonstrate that DL models consistently outperform traditional methods, achieving remarkable accuracy rates such as 98% for brain tumor detection and 96.46% for Parkinson's disease diagnosis. While these AI technologies show immense promise for enhancing clinical decision-making, early disease detection, and patient outcomes, significant implementation barriers persist including poor data quality, model interpretability challenges (the "black box" problem), algorithmic bias concerns, and difficulties integrating AI tools into existing clinical workflows. The study concludes that realizing AI's full potential in healthcare requires interdisciplinary collaboration, robust ethical frameworks, improved data standards, and rigorous real-world validation to ensure responsible and effective implementation.

Five Major Key Points

1. Broad Disease Applications: ML and DL demonstrate versatility across 16 different diseases, from cardiovascular conditions to neurodegenerative disorders, showing consistent high accuracy in diagnosis and prediction tasks.
2. Superior Performance of Deep Learning: DL models, particularly CNNs and RNNs, consistently outperform traditional ML methods in analyzing complex medical data like imaging and time-series, achieving accuracy rates exceeding 95% in many cases.
3. Diverse Data Integration: The technologies effectively utilize multiple healthcare data types including EHRs, medical imaging, genomic information, and physiological signals, enabling comprehensive disease analysis.
4. Critical Implementation Challenges: Despite high research accuracy, major barriers include data quality issues, model interpretability problems, algorithmic bias risks, and difficulties integrating AI into existing clinical workflows.
5. Future Research Roadmap: Success requires interdisciplinary collaboration, development of explainable AI (XAI), establishment of ethical guidelines, and rigorous real-world validation to ensure responsible healthcare implementation.

Three Limitations

1. Limited Disease Scope: The review focuses on only 16 diseases, potentially limiting generalizability to other medical conditions where AI applications might be equally valuable.
2. Data Heterogeneity: Studies utilized datasets with varying quality, size, and annotation standards, making direct performance comparisons difficult and limiting universal applicability of conclusions.
3. Insufficient Clinical Integration Discussion: The review lacks in-depth analysis of practical implementation challenges including regulatory hurdles, clinician adoption barriers, and real-world clinical workflow integration complexities.

Reference

Eur J Med Res . 2025 May 26;30(1):418. doi: 10.1186/s40001-025-02680-7.

Title of the Article:
Unveiling the Potential of Artificial Intelligence in Revolutionizing Disease Diagnosis and Prediction: A Comprehensive Review of Machine Learning and Deep Learning Approaches

Authors:
Hossein Sadr, Mojdeh Nazari, Zeinab Khodaverdian, Ramyar Farzan, Shahrokh Yousefzadeh‑Chabok, Mohammad Taghi Ashoobi, Hossein Hemmati, Amirreza Hendi, Ali Ashraf, Mir Mohsen Pedram, Meysam Hasannejad‑Bibalan, and Mohammad Reza Yamaghani