This review synthesizes over 30 recent studies and reports on AI in hospitals, covering methodology, key use-cases (diagnosis, triage, treatment recommendations, resource management, outcome prediction), illustrative examples, and the ethical/regulatory issues and highlights AI’s potential to improve efficiency and accuracy in patient care.
The research explores the transformative impact of Artificial Intelligence (AI) on healthcare, focusing on its advancements in diagnostics and disease prediction. It traces the evolution of machine learning algorithms from rule‑based systems to sophisticated deep learning techniques, such as CNN for medical imaging and...
Hanan R. Alnjar· International journal of re...· 0 citations
Artificial Intelligence (AI) has been a major factor to transform healthcare delivery and decision-making in
healthcare. Computer vision, machine learning, and deep learning are some of the AI techniques widely used in
workflows of healthcare institutions to promote risk assessment, diagnosis, and care planning. This s...
Sruthi Pushadapu· International Journal of Dru...· 0 citations
This narrative review synthesises clinically relevant evidence on AI-enabled early diagnostics, precision therapeutic pathways, and the principal structural barriers to responsible adoption to find AI is best positioned as an augmentation of clinical judgement rather than a replacement for it.
M. Ahmad, Muhammad Ibrahim Ahmed, Ahmad Sajjad Ashraf· Journal of Advances in Medic...· 0 citations
It is concluded that explainable artificial intelligence improves trust, reliability, and accountability in healthcare systems and is a prerequisite for successful integration of intelligent technologies into clinical practice.
Riya Jacob K· International Journal of Tec...· 0 citations
AI offers considerable potential to reduce costs, increase diagnostic accuracy, personalize care and expand access to quality services, provided its deployment overcomes technical, ethical and regulatory barriers through proactive, adaptive and patient-centered governance.
Laura Beatriz Fernández Feitó, Mariam Alejandra González González, Andy Alonso Pazos et al.· SAP Social AI· 0 citations
Emergency departments (EDs) operate under time pressure, diagnostic uncertainty, and cognitive overload. Artificial intelligence (AI)–driven clinical decision support systems (CDSS) promise to enhance diagnostic accuracy, risk stratification, and workflow efficiency. However, translation from algorithmic performance to...
Ritu Khandelwal, C. Gadkari, Aditya Pundkar et al.· International Journal of Eme...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
