The research landscape is no longer isolated from AI. Academic writing, heavily influenced by the progress of advanced AI writing tools, has been reshaped to reflect more than just human-generated content. Indeed, the \u201cresearch\u201d in a research paper belongs to you. But who is reporting it? Are you overly dependent on generative AI to \u201cfix\u201d all your writing issues? And in this process, is your research manuscript losing the uniqueness upheld by an author\u2019s voice? <\/p>\n\n\n\n
Writing with assistance from tools is not a novel concept. Authors have long since used spelling and grammar checkers to fix errors and enhance language. Yet, when generative AI was introduced in research writing, academic integrity was put under spotlight. The question shifted from \u201cIs this research sound and publishable?\u201d<\/em> to \u201cIs this research paper written by a human or AI?\u201d<\/em> <\/p>\n\n\n\n With growing concerns of excessive (and inappropriate) use of AI tools, the value of a human expert review<\/strong><\/a> <\/strong>is more valuable now than ever! The human gap in AI-powered manuscripts must be prudently managed to balance the scale between artificial intelligence and human intelligence. Read on to know what it takes to prepare publication-ready manuscripts that are AI-assisted yet human-driven. <\/p>\n\n\n\n AI Accuracy in Research Writing\u00a0<\/a><\/p>\n\n\n\n The Human Gap: Critical Checks AI Still Cannot Perform\u00a0<\/a><\/p>\n\n\n\n Practical Examples of How Human Editors Improve AI-Edited Manuscripts\u00a0<\/a><\/p>\n\n\n\n Best Practices for Using AI Responsibly in Research Publishing\u00a0<\/a><\/p>\n\n\n\n Frequently Asked Questions<\/a>\u00a0<\/p>\n\n\n\n Researcher paper writing warrants accountability. So even if you use AI tools for assistance, you should know its benefits and limitations because ultimately you, the author, are responsible for what gets published. <\/p>\n\n\n\n Despite these benefits, there are drawbacks in each of these cases. What if AI alters the intended meaning while refining the language? And will it restructure a sentence to an extent that the original meaning is completely lost? AI tools are only as good as the models they\u2019re trained on. So, in an attempt to enhance your writing, you might end up losing the unique writing style that makes your paper sound like \u201cyou.\u201d <\/p>\n\n\n\n But that\u2019s not all. AI accuracy is not enough to prepare a publication-ready research manuscript. <\/p>\n\n\n\n Areas where AI tools currently lack: <\/p>\n\n\n\n Take the below example where author wanted the original text modified for conciseness and improved readability. Notice how the human-edited version maintains the logical flow and readability while ensuring conciseness, whereas the AI-generated response did not <\/p>\n\n\n\n Original text:<\/strong>\u00a0Cytological diagnosis of\u00a0trichoblastoma\u00a0can be challenging. The\u00a0existence\u00a0of basaloid\u00a0cells that\u00a0may raise concern for malignancy, particularly when\u00a0presenting as\u00a0a large mass in an unusual location.\u00a0Accurate psychological interpretation is clinically important because it directly impacts treatment decisions.<\/mark>\u00a0\u00a0<\/p>\n\n\n\n AI-edited text:\u00a0<\/strong>Trichoblastoma\u00a0can be difficult to diagnose cytologically because its basaloid cells may resemble malignancy, particularly in large tumors at unusual sites. Accurate interpretation\u00a0is\u00a0crucial for\u00a0appropriate treatment\u00a0decisions.\u00a0<\/mark><\/p>\n\n\n\n Human-edited text:\u00a0<\/strong>Trichoblastoma\u00a0is composed of basaloid cells that can mimic malignancy, particularly when it\u00a0presents as\u00a0a large mass\u00a0in an atypical location,\u00a0thereby making its cytological diagnosis challenging.\u00a0<\/mark><\/p>\n\n\n\n Both edits have reduced the word count from the original text. But the human-edited version reads well, following a logical flow of information while ensuring conciseness. The AI-generated response, on the other hand, complicates the readability in the sentence. It also changed the term \u201clarge mass<\/em>\u201d to \u201clarge tumor<\/em>,\u201d altering the original meaning. <\/p>\n\n\n\n This confirms that AI reliability is highly dependent on human judgment. And considering the uncertainty of AI-generated responses that differ based on prompts, researchers are right to remain cautious when using AI tools. <\/p>\n\n\n\n AI tools\u00a0can effectively help you fix basic errors in writing. But when it comes to nuance,\u00a0coherence in research arguments, novelty check,\u00a0and publication significance, human intervention becomes key.\u00a0So, what does\u00a0expert\u00a0human editing<\/strong><\/a>\u00a0provide\u00a0that\u00a0AI misses?\u00a0<\/p>\n\n\n\n An expert review determines whether <\/p>\n\n\n\n AI tools tend to hallucinate\u2014whether it\u2019s fabricating references [1] or facilitating \u201cinfodemic\u201d [2] through disinformation. There have also been cases reported of inappropriate AI-generated images that made it to a peer-reviewed journal [3], leading to embarrassing retractions. These instances have underscored that human oversight is even more crucial in the AI era. An expert review ensures that such instances are identified well before the research paper is submitted for publication, saving time and preventing desk rejections<\/strong><\/a>. <\/p>\n\n\n\n The problem with generative AI is that it could output false information in a highly convincing manner. Although AI tools sometimes warn users that the data must be cross-verified, it fails to provide the next steps to users. Which is why they often accept AI\u2019s claims at face value. <\/p>\n\n\n\n This is where expert human reviewers enter: <\/p>\n\n\n\n Such in-depth assessments are key for verifying whether the data supports the conclusions drawn. A strong combination of AI and human expertise can help develop a workflow<\/a> that produces a sound research manuscript while adhering to ethical practices. <\/p>\n\n\n\n Here\u2019s a table outlining some example sentences to show the difference between AI editing and human editing. When authors draft and edit with AI, the language is refined. But an expert review takes it to a submission-ready stage. <\/p>\n\n\n\n In each of the above cases, you will see the value added by human editor. <\/p>\n\n\n\n AI accuracy is questionable. Relying only on AI-stated information for writing research papers is not advised. AI does polish language, fix grammatical errors, restructure sentences, and help brainstorm ideas. But AI hallucinations and misinformation are often observed, which could severely impact the integrity of research. <\/p>\n\n\n\n So, while you can use AI to\u00a0expedite\u00a0some of the tasks associated with research writing, it is good to\u00a0retain\u00a0human oversight throughout the process.<\/p>\n\n\n\n AI tools can improve research paper drafts by refining author\u2019s writing, fixing grammar and syntax errors, enhancing sentence flow, and reducing word count for conciseness. AI tools are mainly suitable for stylistic copyediting but are not yet designed for evaluating factual accuracy, especially when finalizing research papers. <\/p>\n\n\n\n AI tools can assist editing. But expert human review continues to remain the crux of academic editing. Human editors perform checks that go beyond simple stylistic copyediting. They help <\/p>\n\n\n\n Raw research drafts often contain crude writing with excellent ideas of authors waiting to be presented effectively as a final research manuscript. Even if AI tools are used for editing, a human review is needed to polish the paper<\/a> until it becomes journal ready. <\/p>\n\n\n\n Human editors carry years of experience in the publishing industry, bring their expertise of the subject area, and provide feedback for improvement based on what journal editors and peer reviewers expect to see in a manuscript. That\u2019s why research papers, or any academic document, must be thoroughly vetted by editors who are subject matter experts. <\/p>\n\n\n\nAI Accuracy in Research Writing<\/strong> <\/h2>\n\n\n\n
What AI Does Well<\/strong> <\/h3>\n\n\n\n
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Where AI Falls Short<\/strong> <\/h3>\n\n\n\n
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The Human Gap: Critical Checks AI Still Cannot Perform<\/strong> <\/h2>\n\n\n\n
1. Evaluating scientific rigor<\/strong> <\/h3>\n\n\n\n
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2. Detecting AI hallucinations<\/strong> <\/h3>\n\n\n\n
3. Rechecking AI-generated claims<\/strong> <\/h3>\n\n\n\n
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Practical Examples of How Human Editors Improve AI-Edited Manuscripts<\/strong> <\/h2>\n\n\n\n
Sl. No.<\/strong> <\/td> Before AI (Author\u2019s text)<\/strong> <\/td> After AI (AI-edited text)<\/strong> <\/td> After Expert Human Review<\/strong> <\/td><\/tr> 1<\/strong> <\/td> Air pollution has become\u202fa serious problem\u202fin many countries due to rapid urbanization\u202fand industrial activities.\u202fExposure to polluted air is known to affect\u202fhuman health and is\u202fassociated with respiratory and heart-related diseases.<\/em>\u00a0<\/mark><\/td> Air pollution is a major global health issue and has been linked to many diseases.\u202fMany studies show that air pollution can cause heart problems.<\/em>\u00a0<\/mark><\/td> Air pollution is a growing public health concern, driven by rapid urbanization and industrial expansion.\u202fSubstantial evidence links air pollution exposure to\u202fadverse health outcomes, particularly cardiovascular disease (CVD), which\u202fremains\u202fa leading cause of mortality worldwide.<\/em>\u00a0<\/mark><\/td><\/tr> 2<\/strong> <\/td> Several studies have investigated the relationship between air pollution and cardiovascular outcomes, but the\u00a0results are not always consistent.\u202fSome studies focus only on particulate matter, while\u202fother\u202fexamine gaseous pollutants.<\/em>\u00a0<\/mark><\/td> However,\u202fthere are still gaps in\u00a0understanding\u202fhow different pollutants affect\u00a0cardiovascular diseases.<\/em>\u00a0<\/mark><\/td> Although\u202fprevious\u202fstudies have explored associations between air pollution and cardiovascular outcomes,\u202fresults\u202fremain\u202finconsistent due to variations in pollutant\u00a0types\u202fexamined\u00a0and\u00a0exposure assessment methods.<\/mark>\u00a0<\/em>\u00a0<\/td><\/tr> 3<\/strong> <\/td> Therefore,\u202fthe purpose of this study is to investigate the association between air pollution\u202fexposure\u202fand cardiovascular disease\u202fin adults.<\/mark>\u202f<\/em>\u00a0<\/td> Therefore, this study aims to\u202fanalyze the association between air pollution exposure and cardiovascular outcomes in adults.\u202f<\/em>\u00a0<\/mark><\/td> To address these gaps, the present study\u202finvestigates the association between\u202flong-term exposure to multiple air pollutants and CVD among adults, providing evidence to inform public health policy.<\/em>\u00a0<\/mark><\/td><\/tr> 4<\/strong> <\/td> In the future, more studies should be conducted with larger datasets to confirm the findings.<\/mark>\u202f<\/em>\u00a0<\/td> Future studies should include more data.\u202f<\/em>\u00a0<\/mark><\/td> Future multi-center studies incorporating larger and more diverse datasets are\u202fwarranted\u202fto validate and extend these findings.\u202f<\/em>\u00a0<\/mark><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n \n
Best Practices for Using AI Responsibly in Research Publishing<\/strong> <\/h2>\n\n\n\n
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Frequently Asked Questions<\/strong> <\/h2>\n\n\n\n
1. Is AI accurate for research writing?<\/strong> <\/h3>\n\n\n\n
2.\u00a0Can AI edit research papers?\u00a0<\/strong>\u00a0<\/h3>\n\n\n\n
3. Can AI replace human editors? <\/strong> <\/h3>\n\n\n\n
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4. Why do research papers still need human editing?<\/strong> <\/h3>\n\n\n\n