Comment on: The AI and I: A Collaboration on Competence
Bibliographic record
Abstract
We are writing to bring attention to the limitations of using artificial intelligence (AI) in surgery. While AI has shown great potential in various fields, including medical imaging and diagnostics, its use in surgical procedures is still in its infancy and has significant limitations. First, AI algorithms require large amounts of data to be trained and tested, which is often not available in the surgical setting. This means that AI systems may not be able to adapt to the unique and complex situations that arise during surgery. Second, the accuracy and reliability of AI systems in surgery is still uncertain. Despite advances in technology, AI systems are still prone to errors and can miss important details that may have significant consequences during surgery. Finally, AI systems are not able to replace the critical thinking and decision making skills of trained surgeons. Surgeons need to be able to analyze a wide range of factors and make split-second decisions that AI systems may not be able to replicate. Overall, while AI has the potential to assist surgeons, its limitations should be carefully considered before implementing it in the surgical setting. Further research and development is needed to improve the accuracy and reliability of AI systems in surgery. Sincerely, Martin G. Tolsgaard and Lawrence Grierson P.S. We did not write any of this. An AI did and this was (close to) a Turing test. We typed the following into the OpenAI GTP-3 chatbot, which has recently been published:1write a letter about the limitations of AI in surgery in 200 words for a surgical journal from 2 scientists. Would you have noticed? Large language models such as GPT-3 can write manuscripts eloquently as seen above and even perform reasonably well on USMLE exams.2 Examples of super-human performance in medical imaging diagnosis have already been published for several years.3 However, now these models are beginning to carve further into human domains of expertise by imitating clinical reasoning, surgical expertise, and academic writing—something that we consider core to what makes us different from AI. This leads us to question the nature and understanding of competence. How will our understanding of what it means to write well academically or be an expert surgeon change when an AI sometimes surpasses our own performance? Narrowly focusing on limitations or benefits of AI may not advance our understanding of what surgeons should be able to do in the future and how. Instead, we should consider exploring when and under what circumstances human-AI collaboration works, for whom and why. We need to turn the scientific discourse away from focusing on how AI can replace clinicians and instead explore how best to support their learning and performances through collective competence. Yet, this requires us to take the science of learning and clinical reasoning into account, which is rarely considered in existing AI research.4
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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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.029 | 0.047 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".