Clinically-Oriented AI: Bridging Technology and Patient Care<\/p>\n
Source: Artificial Intelligence in Medicine (Elsevier, ScienceDirect)
\nVolume 169, November 2025 (online first: August 22, 2025)<\/p>\n
Note: Elsevier is the world\u2019s largest publisher of scientific and medical journals, and ScienceDirect is its official online platform for accessing these journals.<\/p>\n
1- Background<\/p>\n
* Artificial intelligence (AI) is being rapidly introduced into healthcare, from diagnostics to hospital workflows.
\n* Most educational resources for clinicians remain overly technical and disconnected from daily clinical practice.
\n* This leaves many physicians unable to use AI tools effectively and safely.<\/p>\n
2- Key Gaps in Current Education<\/p>\n
* Lack of practical focus: Guides are often written like technical manuals, not tailored for clinical use.
\n* Limited specialty relevance: Current materials rarely reflect the realities of individual specialties such as cardiology, neurology, oncology, or radiology.
\n* Clinician exclusion: Doctors are frequently not engaged as central stakeholders in AI adoption and training.<\/p>\n
3- Proposed Solutions<\/p>\n
* Case-Based Learning: Education should use clinical cases tailored to each specialty, helping doctors apply AI to real-world patient care.
\n* Clinician-Friendly Materials: AI guides must be simplified and written for doctors\u2019 daily workflows, not generic computer science texts.
\n* Role of Clinical Informaticians: Physicians trained in Clinical Informatics should lead the design and delivery of AI education, ensuring medical and technical alignment.
\n* Collaborative Development: Professional societies should partner with informaticians to build specialty-specific training programs.
\n* Essential Competencies: Doctors should gain basic knowledge in areas like data collection, validation, generalizability, ROC analysis, and error measures.
\n* 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.<\/p>\n
4- Real-World Impact<\/p>\n
* Clinicians are already facing patient questions such as: \u201cDid AI interpret my mammogram instead of a radiologist?\u201d
\n* Without proper training, many doctors struggle to answer, undermining patient trust and safe AI use.<\/p>\n
5- Broader Insight<\/p>\n
* To ensure AI delivers real clinical value, doctors across all specialties \u2014 from cardiology and neurology to oncology, radiology, and beyond \u2014 need practical, case-based training that makes AI both safe and effective for patients and physicians alike.
\n* The true value of AI in healthcare comes when it is translated into deep, specialty-specific clinical insights \u2014 turning raw data into meaningful guidance that improves decisions for doctors and outcomes for patients.<\/p>\n
6- Conclusion<\/p>\n
* Clinically-oriented AI is not just about advanced technology, but about translating it into meaningful clinical practice.
\n* Building structured, specialty-focused AI education will be essential to make AI safe, effective, and beneficial for both patients and physicians.<\/p>\n