
ChatGPT-4 (GPT-4) has promise to improve disease diagnosis in complex cases by analyzing medical information such as radiology reports, suggests findings published August 14 in JAMA Network Open.
In a study involving different clinical scenarios in six patients who had a delayed diagnosis, researchers led by Yat-Fung Shea, MBBS, from Queen Mary Hospital in Hong Kong found that GPT-4 was accurate in two-thirds of primary diagnoses and over 80% of differential diagnoses, better than that of clinicians alone.
"Overall, GPT-4 has potential clinical use in older patients without a definitive clinical diagnosis after one month, but requires comprehensive entry of demographic and clinical, including radiological and pharmacological, information," Shea and co-authors wrote.
AI's use continues to increase in diagnosing diseases and conditions, relying on imaging data. AI could potentially aid in low-income countries, where specialist care may be few and far in-between.
OpenAI launched GPT-4 in 2023, improving on its base ChatGPT large-language model. In radiology, GPT-4 has shown that it can perform well in decision-making by following criteria from the American College of Radiology (ACR), as well as pass case-based imaging quizzes. The researchers noted that GPT-4 allows for clinical history in daily practice to be analyzed.
Shea and colleagues wanted to find out if GPT-4 could help improve accuracy by clinicians in supplying the most probable diagnosis or suggesting differential diagnoses in complex cases.
The team tested GPT-4, clinicians, and the Isabel DDx Companion diagnostic decision-support system (Isabel Healthcare) in diagnosing six patients aged 65 years and older based on their medical histories, including radiological and pharmacological data. The patients had a delay of definitive diagnosis longer than one month in 2022 and were retrieved after resolution.
The researchers found that GPT-4 outperformed the clinicians and Isabel DDx Companion in making both primary and differential diagnoses.
| Performance of GPT-4, clinicians, and decision support system | |||
| Isabel DDx Companion | Clinicians | GPT-4 | |
| Primary diagnoses | 0% | 33.3% | 66.7% |
| Differential diagnoses | 33.3% | 50% | 83.3% |
The researchers also studied changes in GPT-4's responses and found that certain keywords are needed for the model to make appropriate clinical responses. These included the following: abdominal aortic aneurysm (patient 1), proximal stiffness (patient 2), acid-fast bacilli in urine (patient 3), metronidazole (patient 4), and retroperitoneal lymphadenopathy (patient 6).
The team also reported that GPT-4 could suggest diagnoses that clinicians did not consider before definitive investigations. These included the following: mycotic aneurysm for patient 1 after CT imaging showed an abdominal aortic aneurysm; a drug cause of seizure in patient 5; and the presence of necrotic lymph nodes from a previous CT scan, which should have led to the diagnosis of lymphoma, in patient 6.
The study authors suggested that based on their results, GPT-4 could help increase confidence in diagnosis for clinicians. They also wrote that it could make suggestions like specialists and could be useful in low-income countries lacking resources for specialist care.
The full report can be found here.












![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)








