JMIR MEDICAL INFORMATICS

Szumilas et al

Original Paper

Evaluation of AI-Driven LabTest Checker for Diagnostic
Accuracy and Safety: Prospective Cohort Study
Dawid Szumilas1, MD; Anna Ochmann1, MD; Katarzyna Zięba1, MD; Bartłomiej Bartoszewicz2, PhD; Anna
Kubrak2, MD; Sebastian Makuch3, PhD; Siddarth Agrawal2, PhD, MD; Grzegorz Mazur2,4, Prof Dr, MD; Jerzy
Chudek1, Prof Dr
1

Department of Internal Medicine and Oncological Chemotherapy, Medical University of Silesia, Katowice, Poland
Labplus R&D, Wroclaw, Poland
3
Department of Clinical and Experimental Pathology, Wroclaw Medical University, Wroclaw, Poland
4
Department and Clinic of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Wroclaw,
Poland2

Corresponding Author:
Jerzy Chudek, Prof Dr
Department of Internal Medicine and Oncological Chemotherapy
Medical University of Silesia
Reymonta St. 8
Katowice, 40-027
PolandPhone: +48 32 2591 202
Email: jchudek@sum.edu.pl

Abstract
Background: In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming
medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for
accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory
test interpretation is evident from the millions of monthly searches on test results’ significance. As the potential role of CDSSs
in laboratory diagnostics gains significance, however, more research is needed to explore this area.
Objective: The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS
designed to support medical diagnoses by analyzing both laboratory test results and patients’ medical histories.
Methods: This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stablecondition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for eachparticipant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing
AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard.

Results: The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent
cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying
underlying pathologies.
Conclusions: This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing
to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC’s performance evaluation
highlights the advancements in AI’s role in laboratory medicine.
Trial Registration: ClinicalTrials.gov NCT05813938; https://clinicaltrials.gov/study/NCT05813938
JMIR Med Inform 2024;12:e57162; doi: 10.2196/57162
Keywords: LabTest Checker; CDSS; symptom checker; laboratory testing; AI; assessment; accuracy; artificial intelligence;
health care; medical fields; clinical decision support systems; application; applications; diagnoses; patients; patient; medical
history; tool; tools

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Introduction

checkers in offering reasonable differential diagnoses andsupporting patient decision-making.

In recent times, the implementation of artificial intelligence(AI) within diverse medical domains has garnered significant
attention and practical application [1]. AI-driven technology
has sparked a transformative wave in health care, introducing
inventive solutions to enhance patient care, diagnosis, anddecision-making processes [2]. A notable instance of AI’s
application is evident in the emergence of clinical decision
support systems (CDSSs), direct tools designed to streamline
health care decision-making [3].

A more recent study by Hennemann et al [12] evaluatedthe performance of an app-based symptom checker within therealm of mental disorders. Results revealed that the studied
symptom checker demonstrated moderate-to-good accuracy
in suggesting conditions for mental disorders concerning
formal diagnosis, albeit with variations across disorder
categories and interrater reliability. The symptom checker’s
primary condition suggestion corresponded with interviewbased diagnoses in 51% (25/49) of cases, with at least 1 of
the initial 5 condition suggestions aligning in 69% (34/49)
of cases across the patient cohort. Accuracy fluctuated acrossdisorder categories, ranging from 82% precision for somatoform and related disorders, 65% for affective disorders, to
53% for anxiety disorders. The study concluded that symptom
checkers hold promise as supplementary screening tools inthe diagnostic process. Still, their diagnostic efficacy requires
assessment in more extensive samples and comparison withalternative diagnostic methods.

Laboratory tests are essential in modern health care,providing valuable insight into a patient’s health status andimproving the accuracy of diagnosing medical conditions.
The interpretation of laboratory test results is a complex
process requiring medical expertise and knowledge. However,
the mounting reliance on laboratory testing poses a formidable challenge for health care systems, particularly in regions
where tests are often administered without direct medical
oversight, as seen in Poland [4].
The significance of this challenge is highlighted by thesubstantial volume of inquiries related to laboratory test resultinterpretation, which include identifying potential causes or
implications of certain findings and seeking guidance on thenext steps or actions based on the test results. Data indicate
that, in Poland alone, there are approximately 7 million
monthly searches concerning the significance of laboratory
test results. On a larger scale, within the European Union,
this number escalates to around 82 million monthly searches
based on data from SENUTO and Google AdWords [5].
These cases emphasize the need for effective strategies tomanage laboratory test interpretation in modern health caresettings.
Given the widespread use of laboratory diagnostics,
which includes a wide range of tests that analyze blood,urine, tissues, and other bodily fluids to diagnose andmonitor diseases, assess organ function, and guide treatment
decisions, there is a growing interest in exploring the potential
of CDSSs within this realm. The inherent complexities tied
to test result interpretation underscore this interest. Whilethe efficacy and safety of CDSSs have been demonstrated invarious medical contexts, such as symptom assessment tools[6,7], the integration of CDSSs into laboratory diagnostics
remains underexplored.
Several studies have assessed the effectiveness and safetyof AI-driven symptom checkers, tools designed to aid patientsin self-diagnosing symptoms and making informed healthcare choices [8-10]. These tools use algorithms and databases
to generate potential diagnoses based on user inputs.
A notable study conducted by Semigran et al [11]scrutinized the diagnostic precision of 23 distinct symptom checkers, comparing their outcomes against physician
diagnoses. The investigation disclosed that symptom checkers
achieved accurate diagnoses in 34% of instances, while
physicians achieved 58% accuracy. Despite relatively lower
accuracy, the study underscored the potential of symptom
https://medinform.jmir.org/2024/1/e57162

This paper addresses the status of AI-based technologies
in health care, specifically focusing on implementing CDSSs
in direct-to-patient tools. After emphasizing the importanceof laboratory diagnostics in contemporary health care andthe challenges tied to test result interpretation, we examine
the existing but limited literature concerning CDSSs’ rolein laboratory diagnostics, underscoring the need for further
research and advancement in this domain. The objective of
this study is to evaluate the performance of a novel CDSS
named LabTest Checker (LTC) in a cohort of adult patientsrequiring laboratory testing. The main question it aims toanswer pertains to the accuracy and safety of LTC.

Methods
Description of LTC Technology
LTC is an intricate medical software designed to provide
assistance in the preliminary medical diagnosis process
through the analysis of laboratory test results and comprehensive medical history. By leveraging advanced whitebox machine learning algorithms and data analytics, LTC
empowers patients and health care practitioners to derive
insightful conclusions and make informed decisions. The
AI models were trained on a comprehensive dataset encompassing clinical data from electronic health records, public
repositories, documented case studies, and expert medical knowledge. LTC seamlessly integrates with existing
electronic health record systems, automatically importing
patients’ latest laboratory results. This triggers a dynamic
medical questionnaire presented on a user-friendly tablet
interface, typically completed within 90‐120 seconds, which
delves into the patient’s medical history, symptoms, andpertinent risk factors. Through this methodical scrutiny andcorrelation of pivotal data, LTC effectively evaluates anindividual’s health status and detects potential medical issues,
empowering patients and health care providers to estabJMIR Med Inform 2024 | vol. 12 | e57162 | p. 2
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Szumilas et allish more accurate diagnoses and improve patient care andoutcomes.

Study Setting and Population
The study was conducted at the Emergency Department (ED)of Andrzej Mielecki Public Clinical Hospital in Katowice,
Poland, with 8 specialized departments and a total bed
capacity of 351. This cohort study embraced a prospective
data collection approach. A total of 101 self-referred patients
aged ≥18 years, in stable condition, but requiring comprehensive diagnosis were enrolled between December 22,
2022, and March 31, 2023. Comprehensive diagnosis refers
to cases where diagnosis based solely on subjective evaluation and physical examination is unattainable, necessitating in-depth assessment through laboratory tests. Inclusion
criteria encompassed (1) age ≥18 years and (2) requirementof in-depth laboratory test investigation. The only exclusion
criteria was pregnancy. Trained research staff identified andinvited eligible patients to participate following an initial
medical evaluation to assess eligibility based on the predefined inclusion and exclusion criteria (further details areavailable on ClinicalTrials.gov [13]). The study achieved ahigh response rate of 84.9% (101/119), indicating strong
participant willingness.

Study Design
This prospective cohort study involved 101 patients,
all requiring comprehensive diagnosis beyond subjective
evaluation and physical examination. A panel of bloodlaboratory tests exceeding the routine diagnostic work-up
at the ED, including a lipid profile, erythrocyte sedimentation rate, high-sensitivity C-reactive protein, creatinine,urea, iron, liver enzymes (alanine transaminase, aspartatetransferase, and gamma-glutamyl transferase), sodium,

potassium, glucose, uric acid, thyroid-stimulating hormone,and complete blood count, was conducted for each participant. LTC was used to interpret these results, and its
performance was compared to that of an internal medicine
specialist (JC), who reviewed the urgency categorizations
assigned by ED physicians and the AI-generated suggestions
without prior knowledge of the LTC results. It is important
to clarify that JC did not directly assess patients in the ED.
Instead, attending physicians in the ED assigned the initialurgency category for each patient based on their clinicaljudgment. JC then reviewed these urgency classifications
assigned by the ED physicians, alongside the assessments
generated by the model under study. This 2-pronged approach
aimed to ensure the accuracy of the urgency categorizations
and provide an additional layer of validation.
Patients presenting at the ED underwent laboratory tests
and provided health-related information under a doctor’s
supervision. This encompassed biometric details, medical history, medications, substances used, family history,
symptoms, and prior test results. Based on these data and test
outcomes, AI algorithms suggested underlying pathology anddiagnostic-therapeutic guidance.
Accuracy and safety were assessed by comparing AIgenerated suggestions to experienced doctor (consultant)recommendations, which are considered the gold standard.
The consultant, blinded to the LTC results, categorized theurgency of physician interaction for each test (emergency,
urgency, routine, and self-care; Table 1). Following thisassessment, the LTC results were disclosed to the consultant
to evaluate if adhering to LTC recommendations could avoid
needless medical visits and whether LTC correctly identified
the underlying causes of any abnormal results.

Table 1. Diagnostic and therapeutic recommendations generated by LabTest Checker (LTC) and specialist recommendations were categorized
to assess LTC’s precision. Sensitivity for the emergency category was computed as the ratio of LTC’s correct emergency identifications to thephysician’s emergency identifications: A / (A + B + C + D). Similarly, sensitivity for the urgency category was calculated as F / (E + F + G + H).
Triage accuracy was calculated as (A + F + L + R) / total number of patients in the study. Triage safety as calculated as (A + E + F + J + K + L + N+ O + P + R) / total number of patients in the study.
Urgency category of contact with doctor, assigned by the
consultant
Emergency
Urgency
RoutineSelf-care

Urgency category of contact with doctor, assigned by LTC
Emergency
A
E
J
N

Owing to the technology’s design, certain variables were
excluded from determining pathology identification accuracy:(1) interpretations labeled as urgent or requiring immediatecontact with a doctor were omitted to ensure patient safety
and prioritize triage in emergencies, and (2) interpretations
categorized as “end of diagnostic - no need for doctor
contact” were omitted when results were valid or deviations
were insignificant and did not signify pathology.

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UrgencyB
F
KO

RoutineC
G
L
P

Self-care
D
H
M
R

Ethical Considerations
The study protocol was registered on ClinicalTrials.gov(NCT05813938), and ethical approval was granted by the
Bioethics Committee of the Medical University of Silesia (approval code: PCN/CBN/0052/KB1/115/I/22; approvaldate: November 8, 2022). All patients provided written
informed consent before undergoing screening for study
eligibility. To ensure privacy and confidentiality, all datacollected during the study were anonymized and deidentified
before analysis. Participants received no compensation for
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Szumilas et al

their involvement in the study. The study involved noninvasive procedures, and the primary intervention was the useof LTC to interpret laboratory test results. Participants were
informed that they could seek clarification or assistance frommedical professionals while using LTC.

Statistics
A power analysis was performed to determine the statistical power of this study, considering a total sample sizeof 101 participants in a single group of patients, which
was predetermined in the study design. The power analysis
was conducted using the G*Power software (version 3.1.9.7;
Heinrich-Heine-Universität Düsseldorf). The power analysis
was based on a 1-tailed test with an α level of .05. The effectsize was calculated at 0.36. Using these parameters and thetotal sample size of 101, the power analysis indicated thatthe study would have moderate statistical power to detect asignificant effect size within a single group of patients. The
estimated power achieved with the given sample size was
0.82, indicating that the study had a reasonable likelihood of
detecting meaningful differences within the group.
Outcome measures were prespecified and calculated with
95% CIs. The Wilson score method was used to produce CIs
for sensitivity to emergency, sensitivity to urgency, accuracyof triage, safety of triage, and reduction of unnecessary visits.
Calculations were performed using the statistical software
package Statistica (version 13.0 PL; TIBCO Software Inc).
Analytic data are presented as point estimates and 95% CIs,
with a P value <.05 being considered significant.

Results
In the context of this study, the triage accuracy in the
101-patient cohort was 74.3%, with a safety sensitivity of
100% for identifying emergency cases and a sensitivity of
92.3% for detecting urgent cases. The implementation of
the system led to a noteworthy 41.6% (42/101) reduction in
unnecessary medical visits, and its accuracy in identifying theunderlying pathology was 82.9%.
The system classified patients based on urgency: 9 patientsrequired immediate contact; 41 needed urgent contact; 50
warranted routine contact; and 1 did not necessitate doctor
contact, falling into the self-care category. Analysis by the
consultant revealed disparities in urgency category assignments for 26 patients. Notably, the technology overestimatedurgency for 25 patients, including cases where the consultant
recommended urgent contact, but the technology indicatedimmediate or scheduled contact. However, the technology
inaccurately assessed the urgency for 1 patient, failing toalign with the specialist’s urgent contact suggestion, instead
proposing scheduled contact. These findings collectively
underscore the triage system’s effective urgency categorization while also pinpointing areas for enhancement to improve
precision, diminish disparities, and prevent false negatives.
These findings are detailed in Table 2.

Table 2. Classification outcomes of diagnostic-therapeutic recommendations proposed by LabTest Checker (LTC) and those provided by the
consultant.
Urgency category of contact with doctor, assigned by
the consultant
Emergency
Urgency
RoutineSelf-care

Urgency category of contact with doctor, assigned by LTC

Emergency, n7110Discussion
Principal Findings
The promising results obtained from the evaluation of LTC
show the potential of AI-driven tools in assisting patientsand medical professionals in navigating the complexitiesof laboratory test result interpretation. An accuracy rate of
74.3% demonstrates LTC’s capability to furnish dependable
medical recommendations grounded in blood test results, adevelopment that holds promise for enhancing operational
efficiency in the medical domain. Particularly noteworthy is
LTC’s impressive safety sensitivity of 100% for identifying
emergency cases and a high sensitivity of 92.3% for detecting
urgent cases. These results imply the system’s adeptness in
identifying critical scenarios, aligning with its intended roleof providing secure and precise medical counsel.
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Urgency, n
0
24
15
2

Routine, n
0
1
43
6

Self-care, n0001Comparison to Prior Work and Broader
Implications
The clinical implications of our findings extend beyond
acute care settings. LTC has the potential to revolutionize health care delivery across various domains, including
outpatient clinics, preventive care, and direct-to-consumer
health management.
In outpatient settings, LTC could streamline triage
processes by providing rapid, accurate assessments oflaboratory results, allowing health care providers to prioritize
patients more effectively and potentially reducing the burden
on overstretched health care systems. Additionally, integrating LTC into preventive care programs could empower
individuals to monitor their health proactively, fostering early
detection and intervention for potential health issues.
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JMIR MEDICAL INFORMATICSThe potential impact of LTC on the direct-to-consumer health landscape is equally promising. By providing individuals with accessible and easily understandable
interpretations of their laboratory results, LTC could facilitate
informed decision-making, leading to earlier detection andmore effective management of health conditions. This shift
toward proactive health care could encourage individuals totake greater ownership of their well-being.
While the potential of AI-driven CDSSs such as LTC isevident, it is important to acknowledge the nascent natureof this field. One such study by Gräf et al [14] compared physician and AI-based symptom checker diagnostic
accuracy, where the AI achieved a diagnostic accuracy of
70%. Furthermore, a systematic review of 10 studies revealedconsistently low diagnostic accuracy (range 19%‐37.9%),
while triage accuracy (range 48.8%‐90.1%) was relatively
higher but displayed variability among different symptom
checkers [9]. Our study attempted to adhere to establishedreporting guidelines for machine learning models [15], butmore robust research is necessary to fully understand thecapabilities and limitations of AI-powered CDSSs in diverse
health care settings.

Strengths and Limitations
While the study yielded promising results, several inherent limitations should be acknowledged when assessing theaccuracy and safety of LTC. First, the sample size wasrelatively small, comprising only 101 participants. Although
efforts were taken to ensure analytical strength, a larger andmore diverse sample would enhance the generalizability offindings to the broader population. Furthermore, the study
allowed participants to seek guidance from medical professionals when faced with uncertainties while filling out thequestionnaire, which might not mirror real-world use wheresuch guidance might not be readily accessible. While thisprovision was aimed at optimizing data quality, it could
have potentially introduced an artificial element, warranting
caution when considering the practical implications of thetechnology’s recommendations.

Szumilas et al
Unexpected results during the experiments included
occasional discrepancies between LTC and consultant
assessments, indicating potential areas for model improvement. These discrepancies could stem from complex
interactions between predictor variables that were not fully
captured by the model, suggesting the need for further
refinement of the AI algorithms.
These limitations underscore the necessity for future
research involving more representative samples and realworld use scenarios to validate the robustness and effectiveness of emerging CDSS technologies. By exploring theintersection of AI and laboratory diagnostics, we aim tolay the groundwork for future progress and foster a deeper
comprehension of AI-based CDSSs’ potential in reshaping
laboratory medicine.

Conclusions
In conclusion, our study demonstrates the transformative
potential of integrating AI into laboratory diagnostics throughthe LTC software. The high accuracy and safety sensitivity achieved underscore the ability of AI-driven CDSSs to
identify medical conditions and provide tailored recommendations, enhancing health care decision-making.
As AI continues to evolve within health care, this study
validates the promise of AI in medical diagnostics andhighlights the need for continued research to refine andexpand such tools. By fostering collaboration between AI
experts and laboratory medicine specialists, we can unlock
the full potential of AI-powered CDSSs, paving the way for amore efficient, personalized, and patient-centered approach tohealth care.
The results presented here offer a compelling glimpse into
a future where AI-driven tools play a pivotal role in optimizing patient care and revolutionizing laboratory diagnostics.

Acknowledgments
We would like to acknowledge Karol Dobrzyński for his valuable assistance in preparing this manuscript. This research was
funded by the National Center for Research and Development under submeasure 1.1.1 Industrial Research and Development
Works, the Intelligent Development Operational Program 2014-2020, co-financing agreement POIR.01.01.01-00-0297/19-00
of November 13, 2019.
Data Availability
All data produced in this study are available upon reasonable request to the authors.
Conflicts of Interest
SA is the chief executive officer of Labplus, the company that owns and develops the artificial intelligence (AI)–based LabTest
Checker (LTC). BB is an associate of Labplus. Both SA and BB receive compensation from Labplus and hold equity in thecompany. AK is an employee, also receiving compensation from Labplus. These financial connections could be perceived as apotential conflict of interest. Every effort has been made to ensure that the study design, data analysis, and interpretation havebeen conducted objectively and rigorously. The rest of the authors declare no conflicts of interest.
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Abbreviations
AI: artificial intelligence
CDSS: clinical decision support system
ED: emergency department
LTC: LabTest Checker

Edited by Christian Lovis; peer-reviewed by Ali Al-Asadi, Joseph Walsh, Robert Campbell; submitted 06.02.2024; final
revised version received 22.05.2024; accepted 25.05.2024; published 14.08.2024
Please cite as:
Szumilas D, Ochmann A, Zięba K, Bartoszewicz B, Kubrak A, Makuch S, Agrawal S, Mazur G, Chudek J
Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort StudyJMIR Med Inform 2024;12:e57162
URL: https://medinform.jmir.org/2024/1/e57162
doi: 10.2196/57162© Dawid Szumilas, Anna Ochmann, Katarzyna Zięba, Bartłomiej Bartoszewicz, Anna Kubrak, Sebastian Makuch, SiddarthAgrawal, Grzegorz Mazur, Jerzy Chudek. Originally published in JMIR Medical Informatics (https://medinform.jmir.org),
https://medinform.jmir.org/2024/1/e57162

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