AI in Healthcare Content Creation: A Double-Edged Sword and Scary

Use of AI in Healthcare Content Creation

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. What are Possible Risks and Limitations in the AI Content Creation. Discussion on Published Case Study: Retraction of Published Paper about Spermatogonia Stem Cells. The Path Forward: Responsible for the Use of AI.

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. The emergence of artificial intelligence has revolutionary impact on various fields, for example content creation, entertainment, instant retrieve of past or current available data and health care also. In healthcare AI involvement ranges from knowledge of disease, diagnosis and treatment.

In recent events some notable challenges are faced regarding AI content creation for scientific publication, especially healthcare. In recent days a retraction of paper in the journal “Frontier in Cell and Developmental Biology” in which an AI generated featured image was used. By this incident a critique raised regarding AI content or graphic generating in healthcare system.

Let’s discuss and explore potential or expected pitfalls of AI-Generated content in health care system and lessons from this controversial article publication and best way to use AI-Generated Content in health care system.

Commitment of AI in the Healthcare Content Creation

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. AI has tremendous ability to create content by the regrading healthcare system. AI is capable for provide analyze vast databases, patterns and maximum readable content that is time saving and resourceful. The following are some in which AI content is reliable:

How to Streamline Research:

With use of AI hundreds and thousands of research papers can be summarized within minutes and gaps can also be identified. The researchers are looking for previous built research, the AI content is invaluable for them.

Educational Content Generation

When a nursing educationist is going for the creation of infographics and complex medical procedures for best visual learning. There are many AI tools like CHAT-GPT and DA-LL-E that are already identified as efficient content creating sources.

Easy Accessibility for Multiple Inter Language Conversion

For the creation and translating of multiple language content make AI content more favorite. Other than languages there are some additional sources of special learning for people with disabilities such as Braille for visual impaired and audio summaries for hearing compromised population

Favor the Evidence Based Practice in Nursing

AI content is helpful to assist health care professionals to stay updated with evidence based practice currently available and identify area for improvement for current scenarios. This is done by comprehensive review of currently available database that is very easy by using AI.

What are Possible Risks and Limitations in the AI Content Creation

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. There should be assurance for AI content limits and standards regarding healthcare content. There many controversial opinion that’s retracted the published paper underscores risks. Some of them are discussed below.

Editorial and Scientific Rigor Deficit

Sometimes the AI generated content does not meet the pre-settled standard of rigorous standards of academic publications. The figure in the retracted paper raises the concern of accuracy and reproducibility for given prompt. There is a meticulous verification by scientific research that is not always possible by AI tools to fulfill.

Legal and Ethical Concerns

The healthcare area is bound by ethical and moral boundaries that are questionable for the AI content generating tools.

If AI produces or generates misleading and socially or morally harmful content, then who will be responsible?

However, the issue of plagiarism and copyright will arise when the AI is already based on preexisting knowledge and that knowledge is already an intellectual property of someone human.

Contextual Understanding and Limitations

There is sometime lack of nuanced understanding despite AI tools are sophisticated in their creation or generation of content. It is possible that AI tools misinterpret the given data, fail for context accountability, oversimplify the content or over explain the phenomenon that leads to the mistakes and misleading.

Possible Mistakes

Because AI is based on data in the search engine or their feed source that might be unreal or false or in some cases biased. In such cases when accuracy is crucial the AI feed data may lead to severe consequences.

Discussion on Published Case Study: Retraction of Published Paper about Spermatogonia Stem Cells

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. The published paper “Cellular Functions of Spermatogonia Stem Cells in Relation to JAK/STAT Signaling Pathway” is an example of cautionary tale. This article published on 13^th February 2024, by the Journal “Frontier on Cell and Developmental Biology due to concerns over AI- Generated figures.”

What was Wrong?

Non-Verified Picture: Picture in the article was not according to rigor scientific standards.

Lack of Peer Review: This incident also exposes lack of the peer review process of journal that is necessary before the publication. It is certainly ignored throughout the research process.

Trust Deficient: This event occurrence also hurts the credibility of the authors journal and publishing community.

Actions to be Needed

There is a strict need to provide guidelines regarding use of AI content in the publishing content.

There is always a need for human oversight for every source of content, especially AI generated content.

Impact of this Incidental Learning in Future

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. The following valued lessons are gained from this incident:

Human Oversight is Necessary

There is no and never complement to human expertise. The content, especially in such publications, should be reviewed carefully for AI generation along with traditional plagiarism detection to ensure accuracy and relevancy or content provided to published.

Transparency In Reporting

The author should be bound to reveal if they are using AI tools elaborately so the extent of AI content should be considered before nay ambiguity to be raised. The approach to test the content should be used accordingly. By taking such steps, the reviewer will assess the content more effectively.

Make the Peer Review More Strong

The review committee and process must adapt their peer review process according to AI generated content. And AI content experts should be involved in the AI and data scene for the evaluation of submitted paper.

Development of Ethical Guidelines

There should be clear guidelines regarding the use of AI content in the research Publications. Data Bias, transparency, and accountability should be addressed such issues and guidelines.

The Path Forward: Responsible for the Use of AI

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. We never exclude the use of AI in the academics document and content. The AI is here to stay, and its impact in the growing health care environment.

For The Side of Researcher:

Take AI just as a Tool: Ai is only to enhance the research content not to replace the human writing.

Verification of Outputs by Human:

The cross checking of AI generated content over the reliable resources.

Be Alert and Informed:

The AI user should be enough efficient to understand their own capabilities and limitations.

For the Journal Side:

Be Updated Regarding Submission Guidelines

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. There should be verification of raw data that should be disclosed from the author’s side.

Training for Reviewers:

The reviewers should be equipped enough for the AI content detection and review.

Use of Technology

Provision of further AI tools to detect and modify the data to make it reliable and presentable.

For AI Developers

Focus the Accuracy

There should be separate or specific tools for scientific content creation that should consider accuracy and productivity side by side.

Collaboration With Experts:

There should be a collaboration among publishing team: Researchers, Authors and Publishers to overcome the challenges.

Address Bias:

The AI models should be enough trained for the diverse and high-quality database to reduce such bias.

Conclusion

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. AI provides powerful tools and data base for the content creation process. However, its misuse can lead to mistrust and quality compromised content. This incidence of “Frontier in Cell and Developmental Biology” Paper is served as a reminder for the cautionary use of AI content generating tools.

AI in Healthcare Content Creation: A Double-Edged Sword and Scary. The team of researchers, publishers and developers must work together to establish the standards to ensure clear use of AI for scientific content. This is only way to realize the benefits of AI in healthcare knowledge and data.