Evidence map›Paper›PMID 42172279›Full record

ArticlePloS one2026

A qualitative study to inform the design and implementation of AI-driven diagnosis: Challenges, barriers, and clinical insights of physicians.

Shravya Chitrapady, Pooja Gopal Poojari, Muhammed Rashid, Vijayanarayana Kunhikatta, Muralidhar Varma D, Raviraj V Acharya, Shivshankar K N, Vasudha Devi, Dinesh Acharya U, Sohil Khan and 1 more

Abstract read
In one paragraph

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

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Shravya ChitrapadyDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Pooja Gopal PoojariDepartment of Pharmacy Practice, Srinivas College of Pharmacy, Valachil, Mangalore, India.
Muhammed RashidDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.ORCID https://orcid.org/0000-0002-6390-7764
Vijayanarayana KunhikattaDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Muralidhar Varma DDepartment of Infectious Diseases, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Raviraj V AcharyaDepartment of Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Shivshankar K NDepartment of Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Vasudha DeviDivision of Pharmacology, Department of Basic Medical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Dinesh Acharya USchool of Computer Sciences, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Sohil KhanPharmacotherapeutics and Evidence Based Practice, School of Pharmacy and Medical Sciences, Griffith University, Gold Coast Campus, Southport, Australia.
Girish ThungaDepartment of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.ORCID https://orcid.org/0000-0003-1257-969X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAcute febrile illnesses are significant clinical challenge in tropical regions due to overlapping symptomatology among infections such as dengue, scrub typhus, and leptospirosis. This diagnostic dilemma leads to delays in appropriate treatment and negatively impacted patient outcome due to limited diagnostic tool availability. Recent advancement in artificial intelligence (AI) application in healthcare help in developing diagnostic aiding tool in such clinical dilemma.

aimsStudy aimed to explore physicians' perspectives on diagnostic challenges of tropical Acute-febrile-illnesses and assess perceived utility of AI as a supportive tool in clinical decision-making.

methodsA qualitative interview was conducted using validated semi-structured interview guide using literature and expert opinion and validated by IPR method. To understand the physicians perspective on management infectious diseases in critical care, interview guide administered among ten physicians with 10-30 years of experience across different centres of India. Interviews were audio-recorded, transcribed verbatim, and thematically analyzed using ATLAS.ti software.

resultsFour major themes emerged: clinical experience and practice, diagnostic-challenges, diagnostic-parameters and decision-making, and AI integration in clinical-practice. However, no statistical test can be performed since study mainly focused to understand perception of physicians with respect to managing illness and hence prevalence cannot be determined. Diseases such as dengue, scrub typhus, and leptospirosis were reported as most prevalent, particularly during monsoon seasons. Key diagnostic challenges included overlapping symptoms, diagnostic delays, limited sensitivity/specificity of available tests, and cost constraints. Physicians primarily relied on clinical judgment and laboratory findings, despite the limitations. Most of the participants expressed positive attitudes toward the potential of AI as supportive tool, citing its ability to enhance diagnostic accuracy and streamline clinical workflow with proper validation.

conclusionsGrowing in clinical dilemma AI has significant role in diagnosis of tropical fever with integration of the clinical data & physician perspective can successfully able to incorporate in clinical application.

Indexed as

Artificial IntelligencePhysiciansAdultClinical Decision-MakingDengueFemaleHumansIndiaLeptospirosisMaleQualitative ResearchScrub Typhus

Identifiers

PMID42172279
PMCPMC13196980

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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.