Intelligent Systems in Health: Historical Evolution, Current Development and Future Perspectives
Bibliographic record
Abstract
Digital technologies are rapidly transforming healthcare by integrating networked devices, advanced analytics, telemedicine, and electronic health records. Intelligent systems have evolved from early rule-based medical expert systems to modern AI-driven Intelligent agent-based systems (software systems that autonomously reason, learn, and interact). These systems not only enhance operational efficiency and diagnostic accuracy but also support personalized and proactive patient care. This paper presents a historical overview of intelligent systems in healthcare, compares past and present systems through two detailed tables, and introduces current research and development that focuses on clinical decision making (CDM) in the age of AI. The evolution of intelligent systems in digital health from early medical expert systems like MYCIN to modern data-driven platforms illustrates significant technological progress. Contemporary intelligent systems demonstrate increased adaptability, scalability, and integration with clinical workflows. The introduction of advanced AI frameworks and the application of Explainable AI (XAI) techniques have further enhanced transparency and clinician trust. In particular, we discuss an AI-agent framework that integrates machine learning models with XAI techniques and human feedback to automate medical data analysis and report generation. We conclude with a discussion of the advantages, challenges, and future promise of these systems, emphasizing the importance of ethical leadership, effective security mechanisms and stronger cross-disciplinary collaboration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".