Evidence map›Paper›PMID 42367454›Full record

ArticleCureus2026

Future-Ready Doctors: A Cross-Sectional Study of Undergraduate Knowledge of Artificial Intelligence in Clinical Biochemistry.

Susanna Theophilus Yesupatham, Ankita Kumari, Ravishankar Suryanarayana

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Susanna Theophilus YesupathamBiochemistry, Sri Devaraj Urs Medical College, Sri Devaraj Urs Academy of Higher Education and Research, Kolar, IND.
Ankita KumariBiochemistry, Sri Devaraj Urs Medical College, Sri Devaraj Urs Academy of Higher Education and Research, Kolar, IND.
Ravishankar SuryanarayanaBiostatistics, Sri Devaraj Urs Medical College, Sri Devaraj Urs Academy of Higher Education and Research, Kolar, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly transforming healthcare delivery, particularly in laboratory medicine and clinical biochemistry. Despite its expanding applications, the preparedness of future medical professionals to engage with AI remains uncertain. Although several studies have evaluated awareness of AI among medical students globally, limited research has specifically focused on AI applications in clinical biochemistry and laboratory medicine in the Indian context.

aimThis study aims to appraise the knowledge, perceptions, and readiness regarding AI in clinical biochemistry among undergraduate MBBS students. MATERIAL AND

methodsA cross-sectional, questionnaire-based study was conducted over three months (September-December 2025) at Sri Devaraj Urs Medical College, following approval from the institutional ethics committee. A total of 268 MBBS students from Phase I to Phase III participated. Data was collected using a structured, self-administered Google form questionnaire (Google, Mountain View, CA, USA) covering demographics, knowledge, attitudes, readiness, and perceived barriers related to AI in clinical biochemistry. Data were analyzed using SPSS Statistics version 16 (IBM Corp. Released 2007. IBM SPSS Statistics for Windows, Version 16.0. Armonk, NY: IBM Corp.). Descriptive statistics were expressed as frequencies and percentages. Association between academic phase and responses was analyzed using the chi-square test, and effect size was assessed using Cramer's V. A p-value of <0.05 was considered statistically significant.

resultsAwareness of AI in healthcare among students was high (88.4%, n = 237); however, only 19% (n = 51) had received prior formal training in the use of AI. Knowledge of AI in clinical biochemistry increased progressively across academic phases, yet understanding of advanced laboratory applications remained limited. Students largely perceived AI as a supportive tool that enhances report accuracy, reduces errors, and improves laboratory turnaround time. The highest agreement was observed for the importance of AI knowledge in future medical practice (mean Likert score 4.06), whereas the concept of AI replacing laboratory professionals showed the lowest agreement (2.45). Ethical concerns such as data privacy and governance were widely recognized. More than half of the respondents (52.6%, n = 141) expressed willingness to undergo formal AI training.

conclusionsThe findings highlight a significant gap between awareness and structured competency regarding AI among medical undergraduates. The need for early, phase-appropriate, and ethically grounded AI integration within the undergraduate medical curriculum is essential to prepare future physicians for an AI-enabled healthcare system.

Indexed as

artificial intelligenceclinical biochemistryhealthcare technologymedical educationmedical students

Identifiers

PMID42367454
PMCPMC13309873

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.