Jul 2026· Expert Review of Molecular Diagnostics· Vol 26, pp. 755 - 774· 0 citations· 187 references
Medicine
TL;DR
Artificial Intelligence can be very effective in increasing medical professionals’ knowledge and, consequently, improving patient outcomes, but its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data.
Abstract
ABSTRACT Introduction The use of Artificial Intelligence (AI), especially Machine learning (ML) and Deep learning (DL), has led to a major shift in medical diagnosis. AI can assist medical professionals in medical diagnosis by its unique ability to analyze complex data from multiple sources, including medical images, gene sequences, and Electronic Health Records (EHRs). Areas covered Its application in other clinical processes, such as risk classification, diagnostic workflows, and disease risk prediction from patient symptoms, can also speed up diagnosis, reduce costs, and improve diagnostic outcomes. Expert Opinion However, its effective use in the clinic necessitates addressing concerns about data privacy, rigorous validation, and the development of methods to reduce bias caused by medical data. By addressing these limitations, AI can be very effective in increasing medical professionals’ knowledge and, consequently, improving patient outcomes.
Artificial Intelligence (AI), as a multifaceted tool, has gradually developed into a tool that can assist the medical field. This article discusses AI from two aspects: unimodal and multimodal data diagnosis. Unimodal diagnosis is the starting point and foundation of AI in medical diagnosis, using supervised deep learning models for end-to-end anomaly detection or classification based on specific, structured data types. Multimodal diagnosis constructs multimodal fusion networks to integrate heterogeneous data, achieving comprehensive disease assessment, differential diagnosis, and prognostic prediction. This article demonstrates the current advantages and disadvantages of AI models as diagnostic tools, such as the advantages of efficient diagnosis and comprehensive assessment of rare diseases, as well as the many hidden dangers such as insufficient datasets, limitations of dark knowledge, and privacy risks. This study underscores the necessity of improving data quality, interpretability, and ethical regulation to facilitate the reliable clinical adoption of AI-based diagnostic systems.
Artificial intelligence (AI) is becoming an important technology in modern healthcare because of its ability to analyze large volumes of clinical, biomedical, and patient-generated data. AI-based systems are being applied in disease diagnosis, medical imaging, drug discovery, personalized medicine, clinical decision support, remote monitoring, and healthcare administration. This review examines recent trends in the use of AI in healthcare by analyzing six influential studies published between 2017 and 2024. The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care. However, challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation. The review concludes that AI should be implemented as a supportive tool under appropriate clinical supervision, ethical standards, and regulatory frameworks.
Keywords: artificial intelligence; machine learning; deep learning; explainable AI; Medical Imaging; precision medicine
Unknown authors· International Journal of Tec...· 0 citations
It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.
Vinit Kumar Ramawat, G.PRABHAKARAN, Pinki Das et al.· International journal of com...· 0 citations
Highlights
Artificial intelligence is transforming cardiology by enabling more accurate diagnosis, personalized therapy, and prediction of cardiovascular complications.
The study provides a comprehensive systematization of current approaches to machine learning, neural networks, and big data analytics in clinical cardiology.
Key directions for integrating AI into Russian healthcare are highlighted, considering ethical, legal, and organizational aspects.
Abstract
Modern cardiology is undergoing a rapid digital transformation driven by artificial intelligence (AI). The application of machine learning and deep learning algorithms provides unprecedented opportunities for diagnosis, monitoring, and prediction of cardiovascular diseases (CVDs). This review summarizes current evidence on the integration of AI across key domains of cardiovascular care–from data acquisition and analysis to personalized treatment optimization. The article highlights successful applications of AI in electrocardiogram interpretation, cardiovascular imaging, hemodynamic assessment, and prediction of heart failure exacerbations. Particular attention is paid to ethical, legal, and organizational aspects of AI implementation, including transparency, data security, and clinical accountability. International and national frameworks, such as the EU Artificial Intelligence Act, GDPR, and Russian federal regulations, are discussed as foundations for safe and equitable adoption of AI in healthcare. The review also outlines the Russian Federation’s initiatives in digital health transformation, including the development of domestic diagnostic algorithms and unified medical data repositories. Future perspectives include the use of quantum computing, emotional AI, and integration of digital competencies into medical education. Artificial intelligence is viewed as a transformative tool to enhance diagnostic accuracy, treatment efficiency, and preventive strategies in cardiology, provided that human oversight and clinical validation remain central.
Unknown authors· Complex Issues of Cardiovasc...· 0 citations
Artificial intelligence (AI) has emerged as a transformative force in clinical medicine, reshaping how diseases are diagnosed, treatments are selected, and patient care is delivered. This comprehensive review examines the current state and future trajectory of AI applications across the clinical spectrum, from diagnostic algorithms that match or exceed human expert performance in medical imaging and pathology, to therapeutic decision support systems that optimize treatment selection, drug dosing, and surgical planning. We synthesize evidence from recent studies demonstrating that deep learning models have achieved diagnostic accuracy comparable to or exceeding that of board-certified specialists across multiple specialties, including radiology (AUC 0.92–0.98), pathology (AUC 0.90–0.97), dermatology (AUC 0.91–0.96), and ophthalmology (AUC 0.94–0.99). In therapeutics, AI-Driven dosing algorithms have reduced adverse drug events by 30–45% in prospective studies, while machine learning-based treatment response prediction has demonstrated AUC values of 0.78–0.88 across oncology, cardiology, and psychiatry. However, significant challenges persist, including the gap between algorithmic performance and clinical outcome improvement, the “black box” problem of model interpretability, algorithmic bias, and the paucity of prospective validation studies. We critically examine these barriers and outline future frontiers, including multimodal AI integration, foundation models, causal inference, and Human-AI collaboration. We conclude that while AI has demonstrated remarkable capabilities in controlled research settings, successful clinical translation requires rigorous prospective validation, workflow integration, and a commitment to addressing ethical and practical challenges.
Verena Lengston· Japan Journal of Clinical &a...· 0 citations
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