AI Risk Scores vs HEART Score: Which Better Predicts Chest Pain Outcomes?

Introduction

In the United States, AI risk scores vs HEART score chest pain assessment represents a critical frontier in emergency medicine. Approximately 10 million patients present to emergency departments annually with chest pain, accounting for 5-10% of all ED visits. Although the majority of these patients do not suffer from acute myocardial infarction, about 1% of those discharged with non-specific chest pain still experience major adverse cardiac events within 30 days.

Recent evidence suggests considerable limitations in AI-based risk assessment tools. ChatGPT-4 yielded a different risk score than established clinical tools like TIMI or HEART 45% to 48% of the time when evaluating identical patient data. Despite showing high correlations with these validated tools (r = 0.898 and 0.928 for TIMI and HEART scores, respectively), the distribution of individual AI risk assessments remains troublingly broad. Furthermore, when evaluating a comprehensive 44-variable model, the majority of five ChatGPT-4 models agreed on a diagnosis category only 56% of the time. These findings raise important questions about the reliability of AI chest pain risk assessment tools compared to established scoring systems like HEART, which remains widely used for its strong predictive accuracy and ease of application in clinical practice.

Clinical Relevance of Chest Pain Risk Stratification

Chest pain represents the second most common reason for emergency department (ED) visits across the United States, placing enormous pressure on healthcare systems to rapidly distinguish life-threatening conditions from benign ones. This clinical challenge requires effective risk-stratification methods to optimize patient care while efficiently managing limited resources.

Emergency department burden from non-cardiac chest pain

The scale of chest pain presentations to emergency departments is substantial, with nearly 11 million encounters annually in the United States alone, accounting for approximately 5.5% of all ED visits [1]. However, only a small fraction of these patients actually suffer from acute myocardial infarction. Studies indicate that merely 4% of patients presenting with chest pain ultimately receive an MI diagnosis [2]. In fact, up to 20% may have a cardiac cause for their symptoms, while the vast majority receive a diagnosis of non-cardiac pain [3].

This disparity creates a fundamental diagnostic dilemma for clinicians. A retrospective cohort study examining 8,711 ED visits for non-traumatic chest pain found 37.6% resulted in hospitalization, yet among those hospitalized, only 10.7% (representing just 3.8% of all chest pain visits) had acute myocardial infarction [2]. Moreover, in 29.4% of these hospitalizations, myocardial infarction was ruled out with no cardiac ischemia identified [2].

The burden extends beyond initial presentations. Patients with non-cardiac chest pain (NCCP) frequently return to emergency departments, creating additional strain. One study found that 13.7% of NCCP patients were represented in the ED within 1 year [4]. Independent predictors of frequent re-presentation included a history of COPD (OR 2.06), a history of MI (OR 3.6), and a Charlson comorbidity index ≥1 (OR 1.51) [4].

Although the prognosis for NCCP patients appears generally favorable compared to those with cardiac chest pain (CCP)—with one-year mortality rates of 2.3% versus 7.2% respectively [4]—patient-centered analyses reveal more complex outcomes. In fact, over 40% of NCCP patients experienced persistent biopsychosocial morbidity warranting further clinical attention [4].

Overuse of resources due to precautionary admissions

The fear of missing acute coronary syndrome (ACS) cases has led to defensive medicine practices and resource overutilization. Historically, patients presenting with chest pain have had among the largest variations in hospital admission rates, largely driven by guideline recommendations to secure objective cardiac testing for possible ACS before or within 72 hours of hospital discharge [3].

This cautious approach has resulted in significant economic consequences. The annual cost of chest pain evaluations in the United States exceeds $5 billion [3], with estimates ranging from $10 to $ 13 billion [1]. Additionally, a study examining the impact of aggressive testing found patients who received additional tests accrued approximately $500 more in healthcare costs during their ER visit and $300 more during the 28-day follow-up period [4].

Moreover, patients who received more testing experienced longer hospital stays. The length of stay for patients who received less testing was, on average, 20 hours compared with 28 hours for those who received additional tests [4]. This extra time spent in hospitals places additional strain on already limited healthcare resources.

Notably, research increasingly questions whether this aggressive approach improves outcomes. One multicenter study found no differences in the percentages of patients that underwent stent placement, coronary artery bypass surgery, or experienced major cardiac events between groups that received additional testing versus those who did not [4]. Current evidence fails to identify improvements in patient outcomes associated with hospitalization after an ED evaluation has ruled out acute myocardial infarction [4].

In response, more targeted approaches to risk stratification have emerged. Risk stratification protocols have demonstrated advantages over unstructured physician judgment, which tends to overestimate risk [3]. Two well-validated protocols—the HEART Pathway and the EDACS-ADP—achieve negative predictive values exceeding 99% for 30- to 45-day MACE with specificities ranging from 40% to 60% [3]. These tools help identify truly low-risk patients who can safely be discharged without further testing.

Overview of the HEART Score as a Risk Tool

Unlike other cardiac assessment tools, the HEART score was specifically developed for the evaluation of patients with chest pain in emergency department settings rather than for those with confirmed acute coronary syndrome [5]. Designed for ease of use and rapid application, this tool resembles the Apgar score for newborns in its straightforward approach to clinical decision-making [6].

HEART score components: History, ECG, Age, Risk factors, Troponin

The HEART score comprises five distinct elements, each rated on a scale of 0-2 points, for a total possible score of 0-10 [4]. The acronym HEART represents each component:

History: Evaluated as slightly suspicious (0 points), moderately suspicious (1 point), or highly suspicious (2 points) [4]
ECG: Normal (0 points), non-specific repolarization disturbance (1 point), or significant ST deviation (2 points) [4]
Age: Under 45 years (0 points), 45-64 years (1 point), or 65 years and older (2 points) [4]
Risk factors: None (0 points), 1-2 risk factors (1 point), or 3+ risk factors/history of atherosclerotic disease (2 points) [4]
Troponin: Normal (0 points), 1-3× normal limit (1 point), or >3× normal limit (2 points) [4]

Risk factors considered include hypertension, hypercholesterolemia, diabetes mellitus, obesity (BMI >30), smoking (current or ceased within three months), and family history of cardiovascular disease [4]. Prior atherosclerotic disease, such as myocardial infarction, percutaneous coronary intervention, coronary bypass, stroke, or peripheral arterial disease, also contributes to the risk factor score [4].

Based on total points, patients are classified into three risk categories: low (0-3), intermediate (4-6), and high (7-10) [2]. This stratification directly informs clinical decision-making: low-risk patients are considered candidates for early discharge, intermediate-risk patients are recommended for admission, and high-risk patients are evaluated for early invasive strategies [4].

Validation studies for 30-day MACE prediction

Since its introduction in 2008, numerous validation studies have confirmed the HEART score’s predictive value for major adverse cardiac events (MACE) [2]. Initially tested in a prospective study of 122 patients, the score demonstrated an almost linear relationship between score value and clinical outcomes [2].

Subsequently, a large multicenter validation study involving 2,440 patients across 10 Dutch hospitals compared HEART with other established scoring systems [2]. Remarkably, this study found that among patients classified as low risk (score 0-3), only 1.7% experienced a MACE within 6 weeks [4]. In contrast, intermediate-risk patients (score 4-6) had a 16.6% event rate, and high-risk patients (score 7-10) showed a 50.1% event rate [4].

Statistical analysis consistently demonstrates that the HEART score outperforms other tools. The C-statistic for HEART (0.83) significantly outperforms both TIMI (0.75) and GRACE (0.70) scores (p<0.0001 for both comparisons) [2][6]. This advantage is consistent across different patient populations, including people with diabetes (C-statistic 0.78), women (C-statistic 0.83), and elderly patients aged 75 or older (C-statistic 0.73) [6].

A comprehensive meta-analysis of 30 studies encompassing 44,202 patients found that a HEART score ≥4 had a sensitivity of 95.9% and specificity of 44.6% for predicting MACE [7]. For high-risk scores (≥7), sensitivity was 39.5% and specificity was 95.0% [7].

Likewise, the tool’s versatility extends across different healthcare systems. A recent study in Indian emergency departments found that patients with low HEART scores (0-3) had a MACE incidence of only 0.9%, intermediate-risk patients (4-6) had a 28.6% MACE incidence, and high-risk patients (7-10) had a 89.2% MACE incidence [7].

The HEART score’s clinical utility primarily lies in its ability to safely identify patients suitable for early discharge. Research indicates this approach can reduce unnecessary admissions without compromising patient safety, as evidenced by the extremely low MACE rates (0.9-1.7%) among patients classified as low-risk [4][4].

Designing Simulated Patient Datasets for AI Evaluation

To evaluate AI performance against established clinical tools, researchers created three distinct datasets that simulate patient presentations for chest pain assessment. These datasets provided the foundation for testing ChatGPT-4’s consistency and accuracy compared to traditional risk stratification methods.

TIMI-based dataset with 7 binary variables

The first dataset constructed to compare AI risk scores with established chest pain assessment tools included the seven variables from the Thrombolysis in Myocardial Infarction (TIMI) score for unstable angina or non-ST-elevation myocardial infarction [3]. For effective interaction with ChatGPT-4, researchers encoded each variable in a binary (yes/no) format, including:

Age ≥ 65 years
Three or more coronary artery disease (CAD) risk factors
Known coronary artery disease
Aspirin use within the past seven days
At least two episodes of severe angina in the past 24 hours
ECG ST changes ≥ 0.5 mm
Positive cardiac marker

This binary encoding strategy created a straightforward framework for AI evaluation, as ChatGPT-4 yielded risk assessments that differed from the fixed TIMI score approximately 45-48% of the time in individual cases [1]. The research team generated 10,000 randomized, simulated cases using these variables to ensure robust statistical analysis [1].

HEART-based dataset with 5 categorical variables

The second dataset employed the five HEART score variables for major cardiac events [3]. These variables were encoded as categorical rather than binary values:

History: Classified as slightly suspicious, moderately suspicious, or highly suspicious
ECG: Categorized as normal, non-specific repolarization disturbance, or significant ST deviation
Age: Stratified into three categories (<45 years, 45–64 years, or >64 years)
Risk factors: Grouped as no known risk factors, one or two risk factors, or three or more risk factors
Initial Troponin: Classified as normal, one to three times the normal limit, or more than three times the normal limit

This dataset’s design closely mirrors the actual HEART score implementation in clinical practice, thereby enabling direct comparison between AI-generated risk assessments and established clinical tools. When tested against this dataset, ChatGPT-4 demonstrated a 45-48% disagreement rate with the fixed HEART score [1], which parallels findings from the TIMI dataset evaluation.

44-variable dataset excluding lab results

The third and most comprehensive dataset included forty-four randomized variables pertinent to non-traumatic chest pain presentations [3]. This dataset focused exclusively on history and physical examination findings without laboratory or imaging results, thus representing the initial clinical encounter. Variables encompassed:

Demographic factors: Age (40-90 years), gender, race
Pain characteristics: Duration (in minutes), severity (1-10 scale), quality (substernal, heavy, burning)
Pain triggers and alleviating factors: Exertion, stress, rest, nitroglycerin
Current medications: Aspirin, blood pressure medications, NSAIDs, statins, insulin
Substance use: Cocaine, alcohol, smoking
Medical history: Hypertension, myocardial infarction, coronary artery disease, diabetes, stroke
Associated symptoms: Nausea, dyspnea, palpitations, dizziness
Social factors: Marital status, family history
Physical examination findings: Vital signs, heart and lung assessments, pain reproducibility

This extensive variable set provided a more realistic clinical scenario for assessing AI’s risk stratification capabilities. Notably, when ChatGPT-4 evaluated identical cases using this dataset across multiple runs, it frequently disagreed with itself, returning different assessment levels 44% of the time [1]. The majority of five ChatGPT-4 models agreed on a diagnosis category only 56% of the time when evaluating this more complex dataset [7].

Collectively, these three datasets established a methodical framework for evaluating AI’s consistency and accuracy in chest pain risk assessment relative to validated clinical tools such as the TIMI and HEART scores.

ChatGPT-4 Risk Score Variability Across Identical Inputs

Recent analyses of AI applications in cardiac risk assessment reveal a paradoxical phenomenon: strong statistical correlation coexisting with clinically concerning inconsistency. ChatGPT-4, when tested against established chest pain risk stratification tools, demonstrated both promising alignment and troubling variability.

45–48% disagreement with TIMI and HEART scores

Despite showing high correlations with both the TIMI (r = 0.898) and HEART (r = 0.928) scores, ChatGPT-4’s individual risk assessments diverged markedly from these clinical standards [7]. Across multiple simulation trials, ChatGPT-4 assigned different risk scores than TIMI in 45% of cases and different scores than HEART in 48% of cases [7]. This level of disagreement reflects the random divergence clinicians would encounter if relying on ChatGPT-4 for cardiac risk stratification in actual practice [7].

The distribution pattern of these disagreements reveals even more concerning patterns. For patients with fixed TIMI scores ranging from one to six, ChatGPT-4 produced three to four different risk assessments [8]. Similarly, with HEART scores ranging from 1 to 9, the AI system generated a wide range of alternative scores [8]. As one researcher noted, “It’s not a calculator. It has this randomness factor… It will treat the data one way and, the next time, treat it differently” [9].

Interestingly, the overall statistical relationship between these tools remains strong. The correlation coefficients between TIMI scores and all five ChatGPT-4 models were consistently high and statistically significant (p < 0.001) [7]. Nevertheless, the practical implications of this discordance cannot be overlooked—particularly as clinicians typically make decisions based on individual patient assessments rather than population-level correlations.

The disagreement patterns exhibit another clinically relevant feature: greater variability occurs predominantly in the middle-range scores [4]. This suggests that ChatGPT-4 performs worse precisely where clinical decision-making becomes most challenging—with intermediate-risk patients who often present the greatest diagnostic uncertainty [8].

Intra-model inconsistency across 5 runs

Beyond comparing AI with established clinical tools, researchers evaluated ChatGPT-4’s consistency when presented with identical data across multiple iterations. The results highlighted fundamental reliability concerns within the AI system itself.

Particularly with the 44-variable dataset, ChatGPT-4 demonstrated poor self-consistency. When analyzing identical case information across five independent runs, individual model scores demonstrated low correlation with the average (r = 0.605, R-squared = 0.366) [7]. To illustrate this inconsistency concretely:

For an average score of four, individual model assessments ranged from two to nine [2]
The five ChatGPT-4 models agreed on a diagnosis category only 56% of the time [5]
On the 44-variable dataset, ChatGPT-4 disagreed with its own previous responses 44% of the time [1]

The standard deviation of scores increased systematically with the mean risk level, peaking at the middle range. For example, model scores showed standard deviations of: 1 (0.919), 2 (1.102), 3 (1.334), 4 (1.627), 5 (1.943), 6 (2.018), 7 (1.687), 8 (1.295), 9 (0.953), and 10 (0.658) [7].

Even after normalization to a 10-point scale, individual models differed from the average model 76% of the time [10]. This pattern of inconsistency persisted regardless of the underlying dataset complexity. As one research team noted, “While ChatGPT-4 correlates closely with established risk stratification tools regarding mean scores, its inconsistency when presented with identical patient data on separate occasions raises concerns about its reliability” [5].

The weights assigned to each variable by the five ChatGPT-4 models were statistically similar, as determined by the Kruskal-Wallis test (p = 0.406) [7]. Yet paradoxically, the specific weight values assigned to individual variables differed substantially between models [7]. This contradiction helps explain why ChatGPT-4 could simultaneously demonstrate high population-level correlation yet produce clinically divergent results for individual patients.

In essence, this inconsistency mirrors the fundamental tension in chest pain assessment between population-level statistics and individual patient management. As one researcher remarked, “Given the same data, ChatGPT would give a score of low risk, then next time an intermediate risk, and occasionally, it would go as far as giving a high risk” [4].

Statistical Correlation Between AI and HEART Scores

Quantitative analysis reveals an intriguing statistical relationship between artificial intelligence outputs and established clinical scoring systems for chest pain evaluation. Behind the variability concerns lies a surprisingly robust correlation that warrants closer examination.

Pearson correlation coefficient: r = 0.928

A statistical comparison between ChatGPT-4 and the HEART score shows a remarkably high Pearson correlation coefficient of 0.928 (p < 0.001) [2]. This indicates that, fundamentally, AI risk assessments closely track the established clinical tool at the population level. The correlation coefficients between individual HEART scores and each of the five ChatGPT-4 models tested remained consistently strong, with every instance reaching statistical significance at p < 0.001 [2].

Beyond the ChatGPT-4 evaluation, other AI integration approaches show promising statistical relationships with conventional scoring systems. For instance, one study found that AI-ECG interpretation, when combined with the HEART score, improved risk stratification with a net reclassification improvement of 19.6% (95% CI, 17.38–21.89) [11]. This integrated approach achieved a C-index of 0.926 (95% CI, 0.919–0.933), outperforming the HEART score alone (AUROC 0.877, 95% CI 0.869–0.886) [11].

Other research reveals how age impacts risk scores independently of AI assessment factors. One study found that for each one-year increase in age and each one-unit increase in HEART score (HS), the chance of a higher SYNTAX score (SS) increased by 6% and 28%, respectively (OR 1.063, 95% CI 1.031–1.096, P < 0.001; OR 1.282, 95% CI 1.032–1.591, P = 0.025) [6].

R-squared analysis and distribution spread

The R-squared value between HEART and ChatGPT-4 scores reached 0.861, indicating that approximately 86% of the variance in AI-generated risk assessments can be explained by the established HEART score [2]. Yet paradoxically, this strong population-level correlation coexists with substantial individual case variability.

When examining the distribution patterns of both scoring systems, HEART and ChatGPT-4 scores showed normal distributions. Nevertheless, ChatGPT-4 produced a notably broader distribution of scores than the conventional HEART score [2]. This widened distribution partly explains why, despite high correlation, ChatGPT-4 frequently assigns different risk categories to identical cases.

The distributional differences become especially relevant in clinical contexts, as chest pain risk assessment tools aim to categorize patients accurately into treatment pathways. Several AI-ECG studies highlight this challenge—even as they demonstrate excellent performance statistics. For example, a long short-term memory (LSTM) model achieved impressive discrimination for 30-day major adverse cardiac events (AUC, 0.884; 95% CI, 0.815–0.941) [12]. Similarly, the model showed excellent predictive performance for myocardial infarction (AUC, 0.963; 95% CI, 0.926–0.993) and all-cause mortality (AUC, 0.849; 95% CI, 0.698–0.948) [12].

Essentially, statistical correlation analysis reveals a nuanced picture of AI chest pain assessment tools. At the population level, these tools demonstrate impressive statistical alignment with established clinical scores. Accordingly, their potential utility in clinical settings remains promising. Concurrently, the wider distribution spread across individual cases underscores current limitations in consistency—a crucial consideration for practitioners evaluating individual patients where risk categorization directly impacts treatment decisions.

The fundamental tension between correlation and consistency reminds clinicians that AI risk scores, though statistically aligned with conventional tools like HEART, must be interpreted cautiously. Otherwise, a broader distribution may lead to misclassification of patients into inappropriate risk categories, particularly among those with intermediate risk profiles, where clinical decision-making complexity peaks.

Weight Assignment Differences in AI vs HEART Models

Beneath the surface of statistical correlations between AI and traditional risk tools lies a fundamental discrepancy in how different variables influence final risk scores. These differences help explain why seemingly similar models produce divergent clinical recommendations.

Variable weighting inconsistency across ChatGPT-4 runs.

Analysis of variable weighting across multiple ChatGPT-4 runs reveals a perplexing pattern. Statistically, the weights assigned by five different ChatGPT-4 models were similar, as indicated by Kruskal-Wallis testing (p = 0.406) [2]. Yet paradoxically, examining the specific weight values assigned to individual variables showed substantial differences between models [2]. This inconsistency extended not only to TIMI variables but also to HEART score components (p = 0.277) [2].

When evaluating the history-and-physical-only dataset with 10,000 simulated cases, researchers observed that individual correlations with the average risk score showed considerable variation (r = 0.605, R-squared = 0.366) [2]. Consequently, what might appear as minor weighting differences ultimately produced dramatically divergent clinical assessments.

The practical impact of this weighting variability becomes evident when examining specific cases. For patients with an average score of 4 across multiple AI evaluations, individual model scores ranged from 2 to 9 [2]. This nine-point scale represents the difference between “discharge home” and “immediate cardiac catheterization” in many clinical protocols—an unacceptable variance for critical care decisions.

Examples: Age, ECG changes, Troponin levels

Examining specific variables highlights how AI models differ from established clinical tools in their weighting approaches. Age received the highest weight from the ChatGPT-4 models, accounting for approximately 8% of the overall risk score [2]. Indeed, this emphasis on age differs from traditional HEART scoring, in which age is treated equally alongside other components.

The Diamond and Forrester criteria for chest pain also received substantial weights in AI models:

Pain precipitated by exertion or stress: 5.5% contribution [2]
Pain relieved by rest or nitroglycerin: 5.1% contribution [2]
Substernal location of pain: 5.0% contribution [2]

Meanwhile, certain pain characteristics functioned as protective factors in the AI models, decreasing cardiac risk scores. These included pain reproducible on palpation and burning pain [2]—characteristics traditionally associated with non-cardiac etiologies.

The inconsistency in ECG interpretation across AI evaluations presents another critical difference from the HEART score. Whereas HEART assigns straightforward points for normal findings (0), non-specific changes (1), or significant ST deviation (2), ChatGPT-4 models often disagreed on ECG weighting, creating confusion for borderline cases [2].

Perhaps most concerning for clinical implementation, troponin level weighting—often the most objective component of cardiac risk assessment—showed notable variability across AI evaluations [2]. Traditional HEART scoring assigns points based on clear troponin thresholds, but AI models demonstrated inconsistent weighting of this crucial laboratory value.

Henceforth, these weighting differences explain why ChatGPT-4 would frequently disagree with itself even when presented with identical patient data on separate occasions [5].

Bias and Fairness in AI Risk Scoring

Concerns regarding algorithmic fairness have prompted a thorough investigation into whether AI chest pain risk scores exhibit unintentional bias toward specific demographic groups. Given these points, researchers examined ChatGPT-4’s outputs across various patient characteristics to assess potential disparities in risk assessment and treatment recommendations.

Gender and racial weight analysis in the 44-variable model

Analysis of the 44-variable model revealed subtle but detectable demographic influences on risk scores. On average, being male increased the acute coronary syndrome risk score by 3.2% (p = 0.012 compared to a null hypothesis weight of 0%) [7]. Coupled with this gender effect, being African American increased the risk score by 1.3% on average (p = 0.008 compared to a null hypothesis weight of 0%) [7]. These findings align with broader concerns in healthcare AI, where algorithms have been found to perpetuate existing biases present in training data.

Such patterns mirror more extreme cases documented in other clinical algorithms. For instance, Optum’s healthcare algorithm recommended that individual Black patients receive half the care of white patients with similar scores because Black patients historically spent less on healthcare [13]. Similarly, chest X-ray AI models have demonstrated higher false negative rates for racial minorities [14], potentially delaying treatment for these populations.

As a result, researchers have identified potential solutions to mitigate bias in medical AI, including oversampling minority populations (which reduced disparities by 74.7%) and synthetic data augmentation (reducing disparities by 10.6%) [14]. These approaches achieved fairness gains without reducing overall performance, maintaining consistent AUC measurements across baseline and modified models [14].

No significant diagnostic/test recommendation bias

In the light of these weight differences, researchers evaluated whether ChatGPT-4 demonstrated bias in its actual clinical recommendations—a more pragmatic assessment of algorithmic fairness. Their analyses found no detectable bias in diagnostic suggestions or initial test recommendations across demographic groups [7].

For cardiovascular diagnoses, an ECG was consistently recommended as the initial test, regardless of gender—64% of the time for men and 65% of the time for women [7]. Equally important, this consistency extended to racial categories, with an ECG recommended 65.0% of the time for both African Americans and non-African Americans [7]. This uniformity in recommendations offers reassurance about AI’s potential clinical implementation.

These findings represent an encouraging exception to documented patterns of testing disparities in emergency departments, where studies show 74% of white patients receive painkillers for broken bones versus only 57% of Black patients [13]. At present, physicians appear receptive to ChatGPT-based suggestions, with one study showing AI assistance improved diagnostic accuracy from 47% to 65% in the white male patient group and from 63% to 80% in the Black female patient group [3].

Worth noting, this AI-driven improvement occurred without introducing or exacerbating demographic biases, with both groups showing identical magnitudes of improvement (18%) [3]—suggesting a promising path forward for chest pain risk assessment tools that enhance care without amplifying disparities.

Clinical Implications of AI Inconsistency in Risk Assessment

The reliability gap between statistical correlation and clinical decision-making raises serious concerns for healthcare providers considering AI tools for chest pain risk assessment. Even with promising population-level statistics, individual patient care demands unwavering consistency.

Potential for misclassification in intermediate-risk patients

The greatest vulnerability appears in intermediate-risk patients, where clinical judgment becomes most critical. In borderline cases, ChatGPT-4’s inconsistency is particularly problematic. The expanded distribution of AI scores for mid-range TIMI values creates a dangerous situation in which identical patients may receive drastically different care pathways [7]. Ultimately, this inconsistency could lead to both inappropriate discharges and unnecessary admissions—precisely where precision matters most.

With 30-day MACE rates of merely 0.09% for low-risk HEART scores versus 0.81% for higher-risk scores [12], accurate stratification remains vital. Remarkably, ChatGPT-4 misclassified low-risk patients as higher risk a quarter of the time [7], potentially triggering unwarranted testing and interventions.

Limitations of using ChatGPT-4 as a chest pain risk assessment tool

Beyond intermediate-risk concerns, ChatGPT-4 exhibits fundamental limitations as a clinical tool. Its randomness factor—likely beneficial for natural language tasks—proves detrimental for medical decision-making [1]. Primarily, its internal inconsistency manifests as different risk scores for identical data inputs 44% of the time [1].

Additionally, ChatGPT-4’s diagnostic categorization proved unreliable, with majority agreement among five models occurring just 56% of the time [7]. Often, its test recommendations appeared illogical, such as suggesting upper endoscopy as the initial test for suspected cardiac conditions [7].

Conclusion

The comparative analysis of AI-based risk assessment tools and the HEART score reveals a paradoxical relationship between statistical correlation and clinical applicability. ChatGPT-4 demonstrates remarkably high correlation with established clinical tools (r = 0.928 with HEART score), yet assigns different risk scores in nearly half of all cases. This discrepancy creates a troubling clinical dilemma, particularly for intermediate-risk patients, where precise stratification directly impacts treatment decisions.

Evaluation across multiple datasets exposes fundamental reliability concerns with AI-based assessment. ChatGPT-4 disagreed with itself 44% of the time when presented with identical data, while five separate models reached consensus on diagnosis only 56% of the time. Such inconsistency stands in stark contrast to the HEART score’s established reliability and validated predictive accuracy for 30-day MACE outcomes.

Variable weighting differences help explain these divergent assessments. Although statistically similar at the population level, AI models assign substantially different weights to key clinical factors than traditional scoring systems do. Age, certain pain characteristics, and ECG findings receive notably different emphasis in ChatGPT-4 assessments than in the structured HEART score approach.

Fairness concerns persist throughout the development of healthcare algorithms. Current evidence indicates subtle demographic influences on AI risk scores, with male gender and African American race associated with small but detectable increases in calculated risk. Nevertheless, actual test recommendations demonstrated encouraging consistency across demographic categories, potentially offering a pathway toward equitable implementation.

Chest pain assessment remains a critical challenge in emergency medicine, balancing patient safety against resource utilization. The HEART score, through numerous validation studies, consistently identifies low-risk patients (scores 0-3) who can safely avoid unnecessary testing and admission. Conversely, current AI implementations lack the reliability needed for clinical decision-making despite promising correlation statistics.

Future research must address the reliability gap between population-level correlation and individual patient assessment. Certainly, AI systems hold tremendous potential to enhance risk stratification, but current implementations demonstrate insufficient consistency for standalone clinical use. Until these fundamental reliability issues are resolved, established clinical tools like the HEART score remain the preferred approach to chest pain risk stratification in emergency settings.

Key Takeaways

This comprehensive analysis reveals critical insights about AI versus traditional clinical tools for chest pain assessment in emergency departments, highlighting both promising correlations and concerning reliability gaps.

AI shows strong statistical correlation but poor clinical consistency: ChatGPT-4 correlates highly with HEART scores (r=0.928) yet disagrees 45-48% of the time on individual cases.
Self-inconsistency undermines clinical reliability: AI models disagree with themselves 44% of the time when given identical patient data across multiple runs.
Intermediate-risk patients face the greatest misclassification risk: AI variability peaks in mid-range scores, where clinical decision-making becomes most critical for patient outcomes.
HEART score maintains superior clinical reliability: With validated 30-day MACE prediction and consistent performance, HEART remains the preferred tool for emergency chest pain assessment.
Current AI lacks readiness for standalone clinical use: Despite promising population-level statistics, fundamental reliability issues prevent safe implementation without human oversight.

The tension between impressive correlation statistics and clinical inconsistency represents a crucial lesson for healthcare AI implementation. While AI tools show potential for enhancing chest pain assessment, the HEART score’s proven reliability and ease of use continue to make it the gold standard for emergency department risk stratification until AI consistency improves significantly.

Frequently Asked Questions:

FAQs

Q1. How does the HEART score compare to AI-based risk assessment tools for chest pain? The HEART score demonstrates superior clinical reliability compared to AI tools like ChatGPT-4. While AI shows strong statistical correlation with HEART scores, it lacks consistency in individual case assessments, disagreeing with itself 44% of the time when given identical patient data.

Q2. Can artificial intelligence improve the identification of high-risk cardiac patients? AI models show promise for detecting coronary artery blockages from ECG readings, performing better than expert clinicians in some studies. However, current AI implementations lack the reliability needed for standalone clinical use in chest pain assessment.

Q3. What are the limitations of using the HEART score? The HEART score should not be used in cases with new ST-segment elevation ≥1 mm or other new ECG changes, hypotension, a life expectancy of less than 1 year, or when non-cardiac conditions require admission, as determined by the healthcare provider.

Q4. How is the HEART score calculated for chest pain patients? The HEART score assigns 0-2 points each for History, ECG abnormalities, Age, Risk factors, and Troponin levels. Patients receive a total score of 0-10, with higher scores indicating a greater risk of major adverse cardiac events.

Q5. What are the key differences between AI and traditional scoring systems for chest pain assessment? While AI demonstrates high correlation with traditional tools such as HEART scores, it shows greater variability across individual assessments, especially for intermediate-risk patients. Traditional scoring systems like HEART offer more consistent and validated performance in clinical settings.