Clinically-Oriented AI: Bridging Technology and Patient Care
Clinically-Oriented AI: Bridging Technology and Patient Care
Source: Artificial Intelligence in Medicine (Elsevier, ScienceDirect)
Volume 169, November 2025 (online first: August 22, 2025)
Note: Elsevier is the world’s largest publisher of scientific and medical journals, and ScienceDirect is its official online platform for accessing these journals.
1- Background
* Artificial intelligence (AI) is being rapidly introduced into healthcare, from diagnostics to hospital workflows.
* Most educational resources for clinicians remain overly technical and disconnected from daily clinical practice.
* This leaves many physicians unable to use AI tools effectively and safely.
2- Key Gaps in Current Education
* Lack of practical focus: Guides are often written like technical manuals, not tailored for clinical use.
* Limited specialty relevance: Current materials rarely reflect the realities of individual specialties such as cardiology, neurology, oncology, or radiology.
* Clinician exclusion: Doctors are frequently not engaged as central stakeholders in AI adoption and training.
3- Proposed Solutions
* Case-Based Learning: Education should use clinical cases tailored to each specialty, helping doctors apply AI to real-world patient care.
* Clinician-Friendly Materials: AI guides must be simplified and written for doctors’ daily workflows, not generic computer science texts.
* Role of Clinical Informaticians: Physicians trained in Clinical Informatics should lead the design and delivery of AI education, ensuring medical and technical alignment.
* Collaborative Development: Professional societies should partner with informaticians to build specialty-specific training programs.
* Essential Competencies: Doctors should gain basic knowledge in areas like data collection, validation, generalizability, ROC analysis, and error measures.
* Integration into Medical Curricula: Just as genetics and biostatistics became part of medical education, AI must now be integrated into medical schools and continuing education.
4- Real-World Impact
* Clinicians are already facing patient questions such as: “Did AI interpret my mammogram instead of a radiologist?”
* Without proper training, many doctors struggle to answer, undermining patient trust and safe AI use.
5- Broader Insight
* To ensure AI delivers real clinical value, doctors across all specialties — from cardiology and neurology to oncology, radiology, and beyond — need practical, case-based training that makes AI both safe and effective for patients and physicians alike.
* The true value of AI in healthcare comes when it is translated into deep, specialty-specific clinical insights — turning raw data into meaningful guidance that improves decisions for doctors and outcomes for patients.
6- Conclusion
* Clinically-oriented AI is not just about advanced technology, but about translating it into meaningful clinical practice.
* Building structured, specialty-focused AI education will be essential to make AI safe, effective, and beneficial for both patients and physicians.
🔗 Full article available on ScienceDirect:
http://doi.org/10.1016/j.artmed.2025.103252
