AI in Healthcare Marketing: A Review, Synthesis and Research Agenda
Bibliographic record
Abstract
This study investigates the relationship between sustainable marketing practices and Artificial Intelligence (AI) approaches in the healthcare industry using a bibliometric analysis. The study uses VOSviewer to extract significant themes and trends from a dataset of 99 articles that were published between 2014 and 2023. The global health crisis and the increasing demand for data-driven decision-making in sustainable healthcare practices are the reasons for the highlighted spike in scholarly interest after 2020. Important contributions from eminent scholars, organisations, and nations are analysed, exposing important developments and trends in the field. The various ways that Artificial Intelligence (AI) is being applied to improve operational effectiveness and advance sustainability in the healthcare industry are highlighted by theme clusters like digital healthcare, personalised healthcare services, and Sustainable Development Goals (SDGs). Notable journals are also noted, offering scholars and professionals a useful resource. The study's conclusion outlines the directions for future research and emphasises how AI has the potential to spur sustainability and innovation in the field of healthcare marketing. AI has the potential to influence effective healthcare policymaking frameworks and contribute to the advancement of sustainability within the healthcare sector.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".