Artificial intelligence – AI-savvy care – has the potential to transforming care by leveraging various algorithmic approaches to enhance patient care, streamline operations, and accelerate research.

Machine learning, a foundational AI category, is widely used for predictive analytics, such as forecasting disease outbreaks, identifying patients at risk of developing certain conditions, and optimising treatment plans based on individual patient data.

A specialised subset, deep learning, particularly excels in analyzing unstructured data like medical images (X-rays, MRIs, CT scans) for highly accurate disease detection and diagnosis, often surpassing human capabilities in tasks like tumor identification.

Natural Language Processing (NLP) empowers healthcare systems to extract valuable insights from vast amounts of unstructured text data found in electronic health records, clinical notes, and research papers, facilitating improved clinical documentation, decision support, and patient communication through chatbots.

Large language models (LLMs) are a subset of Natural Language Processing (NLP) that enable advanced automation and human-like text generation for tasks like translation, summarization, and conversation. However, they also present significant risks, including bias, misinformation, and security vulnerabilities.

Furthermore, robotics, often powered by AI, is revolutionising surgical procedures with enhanced precision, assisting with patient care tasks like medication delivery, and improving hospital efficiency by automating repetitive duties.

Finally, reinforcement learning is emerging as a powerful tool for optimising dynamic treatment regimes and resource allocation in real-time, allowing systems to adapt and learn from continuous feedback to improve therapeutic outcomes and operational efficiency.

Guillermo del Toro – Mexican filmmaker and author:

“I’m not afraid of artificial intelligence. I’m afraid of natural stupidity.“

Ask us how we can make a difference in AI adoption!

At the same time, AI is nascent even in innovations hubs like University Medical Centres, with limited use in daily practice and scale, and most tools still being tested. Because of problems like sharing data and fitting into workflows, AI hasn’t boosted productivity much yet. So, invite Care IQ to demonstrate its “tech wizardry with purpose”!

Sources:

• This review highlights the urgent need for a paradigm shift from retrospective model development to prospective, practice-ready AI integration that supports core care delivery. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2836754. The study’s key conclusions underscore that despite the exponential growth of AI research in intensive care medicine—with a 156% increase in publications since 2021—AI applications largely remain in early development stages, offering limited real-world clinical value. A striking 74% of the 1263 included studies fall below technology readiness level (TRL) 5, indicating they have not progressed beyond internal validation. Only 2% of models reached any level of clinical integration, and none achieved full deployment (TRL 9), illustrating a significant disconnect between innovation and implementation. Furthermore, high methodological risk of bias and minimal use of reporting standards continue to hinder trustworthy clinical adoption.

General AI in Healthcare / Machine Learning:

1. Arkoudis NA, Papadakos SP. Machine learning applications in healthcare clinical practice and research. World J Clin Cases. 2025 Jan 6;13(1):99744. doi: 10.12998/wjcc.v13.i1.99744. PMID: 39764535; PMCID: PMC11577516.
2. “Top 10 Applications of Machine Learning in Healthcare.” KMS Healthcare Blog. (Provides a good overview of various ML use cases, including diagnostics, personalised medicine, and smart health records).
3. Ekpe, Nwali & Odii, Agwu & Adene, Gift & Aguzu, Lawrence. (2025). Applications of Artificial Intelligence and Machine Learning in Clinical Practice. 3. 1-15. (Discusses AI-driven solutions for early disease detection, personalised treatment, and epidemic control).

Deep Learning in Medical Imaging:

1. Ahmad, Mughees & Khan, Jaleed & Yousaf, Adeel & Ghuffar, Sajid & Khurshid, Khurram. (2020). Deep Learning: A Breakthrough in Medical Imaging. Current Medical Imaging Reviews. 16. 946 – 956. 10.2174/1573405615666191219100824. (Provides an overview of modern deep learning models in medical image analysis and their tasks).
2. Lee, J. G., et al. (2017). “Deep Learning in Medical Imaging: General Overview.” Journal of the Korean Society of Radiology, 76(2), 99-106. (Discusses deep learning’s impressive performance in mimicking humans for tasks like detecting structural abnormalities in medical imaging).
3. Al-Obeidat Feras , Hafez Wael , Rashid Asrar , Jallo Mahir Khalil , Gador Munier , Cherrez-Ojeda Ivan , Simancas-Racines Daniel “Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis”, Frontiers in Big Data, Volume 7 – 2024, 2025. (Explores how deep learning enhances diagnosis, treatment, and monitoring of conditions through medical image analysis).

Natural Language Processing (NLP) in Healthcare:

1. Al-Araby, H., & Al-Samarraie, H. (2024). “Natural language processing in electronic health records: A review.” Artificial Intelligence in Health, 1(1). (Highlights the critical role of NLP in extracting information from EHRs and various levels of operation).
2. Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput Biol Med. 2023 Mar;155:106649. doi: 10.1016/j.compbiomed.2023.106649. Epub 2023 Feb 10. PMID: 36805219. (Systematic review on NLP for extracting clinical insights from EHRs, discussing ML and DL techniques).
3. Wieland-Jorna Y, van Kooten D, Verheij RA, de Man Y, Francke AL, Oosterveld-Vlug MG. Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review. JAMIA Open. 2024 May 24;7(2):ooae044. doi: 10.1093/jamiaopen/ooae044. PMID: 38798774; PMCID: PMC11126158. (Reviews NLP systems for extracting structured information from unstructured EHR notes).

Robotics in Healthcare:

1. Pescio, Matteo & Kundrat, Dennis & Dagnino, Giulio. (2025). Endovascular robotics: technical advances and future directions. Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy. 34. 1-14. 10.1080/13645706.2025.2454237.
2. Spyt TJ, De Souza AC. Minimally invasive therapy and robotics. Br Med Bull. 2001;59:261-8. doi: 10.1093/bmb/59.1.261. PMID: 11756215. (Examines the development and implementation of robotic systems in healthcare, improving patient care and freeing up professionals).
3. Agrawal, Arun & Soni, Rishi & Gupta, Deepak & Dubey, Gaurav. (2024). The role of robotics in medical science: Advancements, applications, and future directions. Journal of Autonomous Intelligence. 7. 10.32629/jai.v7i3.1008. (Provides an overview of robotics and its various applications useful for healthcare, from surgery to logistics).

Reinforcement Learning in Healthcare:

1. Khan, A. M., et al. (2025). “Reinforcement Learning in Healthcare: Optimizing Treatment Strategies, Dynamic Resource Allocation, and Adaptive Clinical Decision-Making.” International Journal of Computer Applications Technology. (Explores RL as a powerful AI paradigm for optimising complex decision-making processes, including chemotherapy planning and resource allocation).
2. Feng, M. “Advancements, Challenges and Future Prospects of Reinforcement Learning in Healthcare.” DOI: 10.5220/0013205700004568 In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (ECAI 2024), pages 39-44 ISBN: 978-989-758-726-9. (Discusses RL’s ability to adapt and optimise treatment plans dynamically, enhance diagnostic accuracy, and manage healthcare resources efficiently).
3. Rajaraman, P., et al. (2024). “A Primer on Reinforcement Learning in Medicine for Clinicians.” PMC. (Introduces RL concepts and potential applications in clinical practice for personalised and efficient patient care).

Strategic partnership

Strategic partnership Millensys and Care IQ have formalised a strategic partnership to deliver innovative, integrated healthcare solutions in Saudi[…]

on nov 10

Read more

Productivity through Co-Intelligence

Productivity through Co-Intelligence The healthcare landscape is evolving to enhance patient outcomes and relieve pressures on professionals. A transformative[…]

on jul 7

Read more