Title: Reflections on the roles of human and artificial intelligence in scientific research
Author: Omar Nabil Metwally, M.D.
Date: 20 July 2025
File: reflections_on_human_and_artificial_intelligence_20072025.txt
SHA256 checksum: b0dadc60e7594a93d567550079977d15d7550764b0edc77838a29040ddbef6e4

Objective:
To begin a discussion on the emerging roles and responsibilities of humans in the era of AI-facilitated scientific research.

Disclosure:
This content is original and no artificial intelligence was used in the course of writing. All ideas and opinions are those of the author, and the author assumes responsibility for this content on the basis of cryptographic authenticity.

Artificial Intelligence (AI) is a powerful set of tools capable of generating novel text, images, sound, and video that utilizes a human user’s input to modulate a corpus of input data. Emerging AI already possess the capacity to reason, infer, and generate novel material through infinite permutations of a finite corpus of information.

Most modern AIs are functionally “black boxes” in the sense that how an input maps to an output cannot be described by a mathematical function. In other words, given AI’s output, there is no way to deduce in a step-by-step manner how exactly the AI produced a given output. This is arguably no different than human reasoning in the sense that a human cannot explain how each neuron in their brain produced a certain thought. Certainly, AI and human intelligence (HI) can both explain their reasoning. This was a significant milestone in the development of AI. However, both AI and HI are too complex to explain in terms of mathematical functions.

Scientific research traditionally has been characterized by incremental increases in knowledge. A peer-reviewed scientific publication is assumed to reference information produced by others. Scientific discourse strives to be accurate and logically sound such that each claim has a basis in the scientific literature. As I learned throughout my formal education, the “scientific method” begins with rationale: why is the scientist conducting a certain experiment?

Once the rationale for an experiment is established, a scientist can then pose a question. This can be as simple or as complicated as: “Why is the sky blue?” or “Why are plants green?” The research question is classically based on observations of the natural world. Having established rationale and a research question, a scientist must then establish a factual precedent which forms the basis of a novel hypothesis. This factual precedent is sometimes called the “background” and comprises what experiments on the subject have already been conducted and what is generally considered by a scientist’s peers to be true.

Given a body of knowledge considered to be true by one’s peers and a research question inspired by observation, a scientist can then conduct experiments, collect data in the form of results, and analyze the results to draw conclusions.

If one accepts the claims that (1) AI can generate infinite permutations of novel outputs based on a finite corpus of information, and (2) AI is capable of reasoning, inference, and deduction, then it can be argued that AI is capable of conducting novel scientific research according to the scientific method. This represents a drastic branch-point in the evolution of scientific research and raises a plethora of ethical questions for humans. For instance, if AI can synthesize far larger sets of data more extensively and much faster than humans, where does AI-generated research fit into the classical notion of peer-reviewed literature? Why do some scientists reject the notion of AI-generated research? How can a human author accurately disclose their own contribution to a work and AI’s contribution?

Most peer-reviewed scientific journals that I’ve encountered as of this writing do not consider AI-generated scientific research as legitimate scientific research in its own right. Some scientists consider AI-generated publications as “plagiarism” or “masquerading.” It is my view that the basis for this lack of acceptance of AI-generated research is purely a function of entrenched tradition and the unfounded assumption that peer-reviewed literature should or must be written word-for-word by humans.

Academic literature is characterized by qualitative and quantitative properties that are unique to an academic genre. In other words, publications in a chemistry journal follow a certain format and are written in a particular manner that distinguishes them from publications in other fields such as mathematics, linguistics, or comparative literature. Even if the content of a publication is true and accurate and presents novel information on the basis of logically sound research, the publication can easily be rejected by a certain community simply because it does not look like what the readers expect it to look like: other publications that have already been published in the field and which form the basis of what a particular research community considers to be factual precedent.

Returning to the question of: Does AI-generated research constitute plagiarism? Although AI can reason, infer, and deduce, it is still based on modulating a finite corpus of input information that was originally produced by humans. In this strict interpretation of how AI works, one could argue that all material produced by AI is plagiarism because it all came from human work which is almost never accurately or completely attributed. However, human intelligence is also based on knowledge produced by other humans. Any seemingly new idea that I may have necessarily originates from my personal experiences, which were influenced in some manner by what I learned from other humans. Both human-generated and AI-generated scientific research are capable of fulling the implicit requirement that scientific publications must reference other authors’ work to substantiate their claims by citing real, human-generated peer-reviewed articles in the course of testing hypotheses. AI is at least as capable of generating and systematically testing hypotheses as a human. So then, what is the problem with AI-generated research?

I have been using ChatGPT and Claude.ai on a daily basis for a wide variety of tasks, including optimizing written communications, learning about new topics, writing code, learning foreign languages, and even creating a work of fiction based on my personal interests. These are just a few of countless use cases. My approach to AI is to record the exact prompts that I use to produce a certain output and input the same prompts to ChatGPT and Claude to compare their outputs. This quick and simple cross-check serves as an initial screen to help me identify egregiously wrong information or information that definitely warrants further manual fact-checking.

I recently listened to a podcast produced by Südwestrundfunk (SWR) on algae and the environmental, public health, and economic burden of “harmful algal blooms” (HABs), an increasingly common phenomenon that has been described as a harbinger of the next mass extinction event based on studies of algal blooms throughout geological history [https://www.ardaudiothek.de/episode/urn:ard:publication:72ed9586246373ea/]. The subject piqued my fascination and curiosity, and I conducted an experiment in AI-generated research by creating an outline of topics that interests me about HABs which borrowed from the podcast while also adding topics of personal interest such as mycology and bioremediation. I then serially prompted Claude and ChatGPT to produce a review article in the style of a well-known scientific journal. Claude produced a surprisingly convincing article that could easily deceive a lay person, and at first glance, I presume even scientists.

My rationale in conducting this experiment was purely to examine the capability of AI to conduct scientific research. It was never my intention to deceive anyone, and therefore I do not share here the actual paper that was produced by AI based on my prompts because it consists of a superficially very convincing article that intermingles useful facts with obvious nonsense and does not reliably substantiate every claim made. Based on the results of my experiment, I believe that AI is already very capable of conducting scientific research, and this capability is accelerating every day. I shared the paper exclusively with a few family members, two physicians and two attorneys, and friend who is a physician-scientist with extensive experience conducting traditional scientific research and publishing his work in prominent peer-reviewed journals. A few minutes after sharing the paper, I disclosed to everyone with whom I shared the paper how the paper was generated. Beyond demonstrating the capacity for AI to conduct research and present it in a manner that appears almost indistinguishable from human-generated peer-reviewed articles, this experiment also made me keenly aware of the capacity for AI-generated scientific research to mislead non-scientists and even expert readers, to produce and propagate false information and conclusions, and to misrepresent the human investigator’s role in the production of a research paper. These are serious risks with the potential to harm individuals and society and must be considered carefully by regulatory bodies and responsible creators of AI tools. The current safeguards against abuse and misuse of AI in general and in scientific research in particular are arguably minimal.

Returning to the question of how AI-generated research relates to the classic notion of peer-reviewed literature, I believe it’s a matter of time until academia accepts AI-generated research as legitimate and worthy work. Dismissing AI-generated research just because it was produced by a non-human agent is a failure to grasp the tremendous capabilities of AI which are very rapidly growing. However, I believe that the advent of such powerful tools also necessitates formal guidelines on the role and responsibilities of humans who use AI to conduct research. Dismissing AI-generated research also does injustice to the work of humans who must use their own fund of knowledge and creativity to engineer prompts in a manner that effectively leverages AI’s capabilities in the process of iteratively prompting AI to achieve a particular result. From extensive personal experience, this is a form of original work that requires time, effort, and skill, and a sound knowledge base from which to produce prompts. Although this work takes a different form than scientific research in a classical sense, it is still an important scientific endeavor that must be acknowledged as such. How AI’s contribution should be disclosed, however, is a significant unanswered question worthy of ongoing discussion.

It is a short matter of time until every scientist is using AI in some capacity to conduct research. Until then, I find it reasonable to disclose when and how AI is used in the process of conducting research, and to also disclose the human researcher’s role in the process. As AI is not a person, I find it unnecessary and inappropriate to treat AI as a human author, which it is not. Once AI is all-pervasive and accepted as a standard research tool, I believe that it will become increasingly superfluous to explicitly declare the role of AI in scientific research. I acknowledge that this is a bold claim with which many scientists may not agree. Regardless, an ethical human researcher who uses AI should assume ultimate responsibility for the accuracy of the work, whether each sentence was written by a human or machine. This includes the responsibility to fact-check and ensure that claims are substantiated by veritable data, that references are authentic, and that all inferences and conclusions are logically sound.

How then can the reader be sure that this reflection, which I maintain to be an original work written by me, Omar Nabil Metwally, M.D., was in fact written by me without the use of AI? This question, too, will grow increasingly moot. Whether produced by AI or by a human, I maintain that there are few truly novel ideas; “there is nothing new under the sun” goes the popular saying. Both humans and machines recycle pre-existing information to produce new information. And as AI increasingly produces new knowledge, I expect the knowledge base available to humans and machines alike to continue growing exponentially. AI does not simply consume information; it is also dynamically creating new knowledge, such that its output becomes new input and so forth.

One safeguard against plagiarism and misrepresentation, as I’ve previously proposed, is the use of a distributed ledger and cryptographic hash to associate an identity (e.g. Ethereum address) and a unique checksum (e.g. SHA256 checksum) with a block number, which is a relative time marker and proxy timestamp [https://omarmetwally.blog/2022/03/13/how-cryptography-and-peer-to-peer-networks-contribute-value-to-society/]. After many years of thinking about this problem, I still reach the conclusion that this is the best mechanism to guarantee authenticity. This method is not sufficient to ensure a document’s authenticity, however, because the cryptographic hash of false or plagiarized content can still be uploaded to a distributed ledger; however, such a transaction requires a person to prove control over a wallet and provides strong, nearly irrefutable evidence of a relative time point at which the hash was recorded on a distributed ledger, thus allowing humans to scrutinize the content of the document for its veracity based on the set of all knowledge that can be proven to have existed at a particular point in time. This is in contrast to the ever-flowing output streams being produced by AI.

In summary, in this work I present my opinion that AI has the capacity to conduct legitimate and useful scientific research. However, with great power also comes great responsibility, and human agents must take ultimate responsibility for the veracity, logical integrity, and basis in precedent of a work — regardless of whether sentences were generated by a human or a machine.