Accuracy is inaccurate
Why a focus on diagnostic accuracy for medical chatbot AIs will not lead to improved health outcomes
Bibliographic Data
| ID | 21504492 |
|---|---|
| Authors | Stephen R Milford (0000-0002-7325-9940, Institute for Biomedical Ethics Basel University Basel Switzerland, corresponding author) |
| Year | 2025 |
| Volume | 39 |
| Issue | 2 |
| Pages | 163-169 |
| Publication date | 2025-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Bioethics (JOURNAL) |
| Journal identifiers | ISSN: 0269-9702 • E-ISSN: 1467-8519 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bioe.13365 |
| PMID | 39474916 |
| OpenAlex | W4403898799 |
| Language | EN |
| Citations received | 2 |
Since its launch in November 2022, ChatGPT has become a global phenomenon, sparking widespread public interest in chatbot artificial intelligences (AIs) generally. While not approved for medical use, it is capable of passing all three United States medical licensing exams and offers diagnostic accuracy comparable to a human doctor. It seems inevitable that it, and tools like it, are and will be used by the general public to provide medical diagnostic information or treatment plans. Before we are taken in by the promise of a golden age for chatbot medical AIs, it would be wise to consider the implications of using these tools as either supplements to, or substitutes for, human doctors. With the rise of publicly available chatbot AIs, there has been a keen focus on research into the diagnostic accuracy of these tools. This, however, has left a notable gap in our understanding of the implications for health outcomes of these tools. Diagnosis accuracy is only part of good health care. For example, crucial to positive health outcomes is the doctor–patient relationship. This paper challenges the recent focus on diagnostic accuracy by drawing attention to the causal relationship between doctor–patient relationships and health outcomes arguing that chatbot AIs may even hinder outcomes in numerous ways including subtracting the elements of perception and observation that are crucial to clinical consultations. The paper offers brief suggestions to improve chatbot medical AIs so as to positively impact health outcomes
Chatbot · Health care · Medical education · Political science · Public health · Artificial Intelligence in Healthcare and Education · Computer Science · Healthcare cost, quality, practices · Machine Learning in Healthcare · Medicine · Nursing · Psychology · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |