New Technologies and New Challenges: What Effect Will ChatGPT Have on Plastic Surgery Research?
Bibliographic record
Abstract
ChatGPT is one of many similar technologies that are poised to have a significant impact on the field of plastic surgery research. While these technologies have the potential to provide valuable insights and accelerate the pace of discovery, they may also bring with them a range of potential problems and challenges that must be carefully considered. As artificial intelligence (AI) language models, these technologies have potential benefits for plastic surgery research. Their primary advantage is the ability to analyze vast data sets and identify patterns and relationships that may not be immediately apparent to researchers.1 These tools may help plastic surgeons identify new risk factors, develop new treatments and interventions, and improve patient outcomes. Other potential benefits include enhanced communication and language translation. However, AI also has the potential to generate misinformation through fake reviews of plastic surgery literature and flawed research findings in plastic surgery.2 One potential problem is the quality of data being analyzed. If the data used to train these models are biased or incomplete, then the insights generated by these models may be similarly flawed. Furthermore, although the technology can generate a large volume of text quickly, it may not provide accurate analysis or interpretation of the data. This could lead to researchers relying on weak data to draw conclusions, inaccurate information being disseminated to the public, and serious consequences for patients and surgeons alike.3 There is also the risk that AI language models could be used to automate research processes that should involve human judgment and expertise, leading to oversimplified or incomplete analyses and undermining the role of experts in plastic surgery. Although these technologies can provide valuable insights and suggestions, they cannot replace the expertise and critical thinking skills of human researchers and surgeons. It is important to consider the broader social and ethical implications of using these technologies in plastic surgery research. These include concerns related to patient privacy and informed consent. If these models are trained on patient data, then there is a risk that patient data could be used subsequently without the patients’ knowledge or consent. This can lead to serious breaches of patient privacy and trust and can undermine the integrity of the research process.4 To address these potential challenges, it is important that the plastic surgery community work together to establish clear guidelines and best practices for their use. This may involve engaging in public debate about the role of technology in plastic surgery research, developing standards for data collection and analysis, keeping patients fully informed about how their data will be used, and establishing protocols for ensuring the accuracy and reliability of the insights generated by these models. As the pace of technological innovation accelerates, it is important to assess the potential impact of these tools, so that they are used in ways that align with the values and principles of the plastic surgery community, to ensure that the use of AI language models in the field of plastic surgery research is beneficial and responsible. DISCLOSURE The authors have no financial disclosures or conflicts of interest to declare. ACKNOWLEDGMENT To illustrate the point of this article, the authors used ChatGPT in the preparation of this article. They would like to thank its developers for the potential to advance the scientific conversation in our field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".