Advancing personalised and precision medicine through artificial intelligence: Current insights and future directions
Bibliographic record
Abstract
The convergence of artificial intelligence (AI) and precision medicine is transforming healthcare by introducing a patient-centred, data-driven approach to treatment. Precision medicine, which tailors medical care based on individual characteristics, addresses the complexity and heterogeneity of diseases. The integration of AI into this field has unlocked unprecedented potential for enhancing disease management and advancing personalised care. AI leverages extensive datasets, including genomic sequences, clinical records and molecular profiles, to identify patterns and predict outcomes with remarkable accuracy. Its capabilities extend beyond automation, functioning as a critical tool for informed clinical decision-making. By analysing complex molecular data, AI enhances diagnostic precision through the detection of subtle biomarkers and anomalies frequently overlooked by traditional methods. Machine learning-powered predictive analytics further empower clinicians by forecasting disease progression and guiding treatment personalisation. Practical applications of AI-driven precision medicine are already evident in clinical settings. From diagnosing rare genetic disorders to optimising drug therapies based on genetic profiles, AI is fundamentally reshaping patient care. However, critical challenges, including ethical considerations, data privacy and the need for transparent algorithms, persist. This review examines the synergistic relationship between AI and precision medicine, highlighting ongoing research, technological innovations and interdisciplinary collaboration. Together, these advancements herald a transformative era in healthcare, paving the way for highly personalised and effective therapeutic strategies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".