Can artificial intelligence replace biochemists? A study comparing interpretation of thyroid function test results by ChatGPT and Google Bard to practising biochemists

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Abstract

Background

Public awareness of artificial intelligence (AI) is increasing and this novel technology is being used for a range of everyday tasks and more specialist clinical applications. On a background of increasing waits for GP appointments alongside patient access to laboratory test results through the NHS app, this study aimed to assess the accuracy and safety of two AI tools, ChatGPT and Google Bard, in providing interpretation of thyroid function test results as if posed by laboratory scientists or patients.

Methods

Fifteen fictional cases were presented to a team of clinicians and clinical scientists to produce a consensus opinion. The cases were then presented to ChatGPT and Google Bard as though from healthcare providers and from patients. The responses were categorized as correct, partially correct or incorrect compared to consensus opinion and the advice assessed for safety to patients.

Results

Of the 15 cases presented, ChatGPT and Google Bard correctly interpreted only 33.3% and 20.0% of cases, respectively. When queries were posed as a patient, 66.7% of ChatGPT responses were safe compared to 60.0% of Google Bard responses. Both AI tools were able to identify primary hypothyroidism and hyperthyroidism but failed to identify subclinical presentations, non-thyroidal illness or secondary hypothyroidism.

Conclusions

This study has demonstrated that AI tools do not currently have the capacity to generate consistently correct interpretation and safe advice to patients and should not be used as an alternative to a consultation with a qualified medical professional. Available AI in its current form cannot replace human clinical knowledge in this scenario.

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Most cited references 15

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Dissecting racial bias in an algorithm used to manage the health of populations

Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla … (2023)

We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.

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Andre Esteva, Alexandre Robicquet, Bharath Ramsundar … (2019)

Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.