One key approachis to promote transparency and accountability in thedevelopment and use of AI systems.

In light of these challenges, it is important to continue toengage in ongoing dialogue and collaboration betweenstakeholders

External ethical review

Bias

Interdisciplinary collaboration

The use of AI in thiscontext can perpetuate existing biases in society and leadto discrimination against certain groups.

REMEDIAL MEASURES IN UTILISINGARTIFICIAL INTELLIGENCE FOR WRITINGRESEARCH PROPOSALS

BIOETHICAL ISSUES IN USING ARTIFICIALINTELLIGENCE FOR WRITING RESEARCHPROPOSALS

THE DISADVANTAGES OF USING ARTIFICIALINTELLIGENCE IN WRITING RESEARCHPROTOCOLS

THE ADVANTAGES OF USING ARTIFICIALINTELLIGENCE IN WRITING RESEARCHPROPOSALS

This paper aims to explore the potential ethical implicationsof using AI in research proposal writing for academic andclinical trials and to propose guidelines for the responsibleand ethical use of AI in this context.

Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generatinghypotheses, identifying literature, and suggesting methods for data collection and analysis. However, theuse of AI in research proposal writing raises important bioethical implications, including the unintentionalpropagation of bias and questions about the role of human expertise and judgment in the research process.This paper explores the ethical implications of using AI in research proposal writing and proposes guidelinesfor the responsible and ethical use of AI in this context. The paper will review the potential benefits andchallenges associated with using AI in research proposal writing, discuss the role of human expertise andjudgment, and propose guidelines for promoting transparency and accountability in developing and usingAI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will requireongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency,accountability, and ethical principles.

With cautious implementation and robustoversight, the role of AI in research can be transformative,but in the absence of such frameworks, it may jeopardizethe very foundation of evidence on which both evidence-based medicine and science as a whole rely.

Determiningwhether an idea is truly novel or merely a repetition canbe challenging

AI tools lack thediscernment and judgment required for such decisions.

Growing evidence suggests that chatbots may fabricatereferences—citations that appear to be legitimate butactually point to articles and journals that do not exist.In one study, when ChatGPT-3.5 was asked to generatescientific content with references, more than 55% of thesources it provided were entirely fabricated. Althoughnewer models like GPT-4 have shown improvedperformance, they still produce fictitious references inapproximately 18% of cases.

AI’s inability toaccurately grasp the logic of citation.

Artificial intelligence (AI) is rapidly integrating intoresearch landscape, promising to revolutionize the waystudies are conducted. From accelerating literaturereviews to analyzing complex datasets, AI has thepotential to significantly enhance the efficiency of coreresearch activities. AI tools have been quickly embracedby researchers across various disciplines. Just a fewmonths after the introduction of some of these tools,they were even credited as co-authors in several scientificarticles and preprints.However, these powerful tools also introduce newchallenges to research integrity. The dual nature ofAI—its potential to either strengthen or undermine thequality of evidence—is becoming increasingly recognized.Enthusiasm for AI’s capabilities must be tempered withcaution as uninformed or improper application mayerode the foundation of evidence upon which clinicalpractice relies. Although AI offers substantial benefitsin generating and synthesizing evidence, it can equallycompromise research quality if researchers are notadequately informed about when, how, and where its useis appropriate.