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TRUVACE RECORD VERSION
record: TRV-2026-1252
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-02T14:19:03.832465Z
status: published
lens: g_space
sector: health
headline: Revolutionizing healthcare: the role of artificial intelligence in clinical practice
dek: INTRODUCTION: Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE: This review art…
gain_title: Integrating AI into clinical practice can improve disease diagnosis, treatment selection, and lab testing by leveraging large datasets to increase accuracy, reduce costs, save time, and minimize human errors.
problem_title: (none)
trace_subject: (none)
gain_reading: Integrating AI into clinical practice can improve disease diagnosis, treatment selection, and lab testing by leveraging large datasets to increase accuracy, reduce costs, save time, and minimize human errors.
gain_evidence: AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. | Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. | AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects.
problem_reading: (none)
problem_evidence: (none)
quick_read: Published September 22, 2023, this peer-reviewed review in BMC Medical Education surveys the current state of AI in clinical practice, covering disease diagnosis, treatment recommendations, patient engagement, and laboratory testing. It reports that AI tools can use large datasets to find patterns and potentially surpass human performance in some aspects.

The significance lies in framing AI not as simple task automation but as technology that could enhance care quality, accuracy, and efficiency across settings, while flagging that responsible adoption depends on addressing privacy, bias, legal, and workforce expertise issues that remain unresolved in the literature reviewed.
limitation: Review was limited to English-language articles and is a literature overview, not a primary clinical trial, leaving implementation risks unresolved.
tag: Evidence-backed gain
key_points: Review analyzed PubMed/Medline, Scopus, and EMBASE literature with no time constraints to assess AI impact in healthcare settings. | Potential applications include personalized medicine, optimizing medication dosages, population health management, virtual health assistants, and mental health support. | Authors note AI is about developing technologies that can enhance patient care across healthcare settings rather than simply automating tasks.
rundown: The review searched indexed literature including PubMed/Medline, Scopus, and EMBASE with no time limits but restricted to English, asking about impact and potential outcomes of AI in healthcare settings.

Results describe AI tools leveraging large datasets to identify patterns, with claimed benefits for personalized treatment plans, clinical decision-making, patient education, and patient-physician trust, alongside persistent concerns about privacy, bias, and human oversight.
sources:
- peer_reviewed | BMC Medical Education | https://doi.org/10.1186/s12909-023-04698-z | 2023-09-22
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