I have been toying around in my mind to do things a little bit differently. One day on my morning commute while playing one of the many podcasts in my endless queue, I realized I was already consuming hours of professional information (as well as entertainment) in audio form. Often I launch a YouTube video, set my phone in my cup holder and listen. That’s when it clicked: if I’m consuming information more and more this way, others probably are too. So what happens if I take my blog and extend my thoughts beyond page.
So I have decided to launch The Krafty Librarian YouTube Channel. Sit back listen and hopefully enjoy it. Be aware there will be some awkward first takes, wrestling with video apps, and second-guessing. This professional experiment is one where the goal isn’t perfection, but connection.
I am not abandoning this blog, but merely stretching into a new medium.
Below are my first three videos. If you like them and want to be updated when I drop a new one, please subscribe and hit the like button (see I got the lingo down). Leave a comment on a topic you would like to cover and I will try and get to it. And click here if you are interested in getting the Librarians: The OG Prompt Engineers t-shirt featured in the 3rd video.
My first video introducing the channel. -It’s a new format but same opinions of The Krafty Librarian. In my first video I am introducing the channel and sharing what you can expect going forward. If you have followed my blog you’ll recognize the same theme, just in a conversational format.
My thoughts on AI. -Is AI important to you? Why or why not? I recognize AI is hear and I think it can be helpful but it still has a long way to go because it isn’t searching what you think it is searching.
Librarians are the OG Prompt Engineers. – Librarians didn’t stumble into prompt engineering, we’ve been doing it for decades through expert searching, structured queries, and critical evaluation. In this video, I break down why librarian search skills can map to AI. If you think prompt engineering is new, think again, librarians are the original prompt engineers.
OpenEvidence is an AI-powered medical search platform launched in 2022 by Harvard‑affiliated founders Daniel Nadler and Zachary Ziegler, and cultivated via the Mayo Clinic Platform Accelerate program. It claims to sift through peer‑reviewed sources such as NEJM and JAMA, providing AI‑generated answers with citations. Free access is granted to verified U.S. healthcare providers, funded by advertising.
OpenEvidence reportedly scored a perfect 100% on the USMLE and has secured $210M in funding and carries $3.5B valuation which is attributed to rapid adoption by U.S. physicians according to Forbes. It seeks to address the problem of information overload and the sorting through it all that physicians face to stay up to date. Because it is free and it isn’t considered a diagnostic tool (even though I think many doctors can be using it that way), OpenEvidence doesn’t require FDA approval and it doesn’t have to compete for subscription dollars (individual or institutional) like other products such as DynaMed, UpToDate, and ClinicalKey.
Instead of relying on subscription dollars, OpenEvidence relies on advertising for income. With over 430,000 doctors registered and an additional 65,000 per month (according to the Forbes article), those are a lot of eyes and information that can be used by pharma and other medical advertisers. OpenEvidence does say, they do not share personalized information. So, while an individual user’s information is not shared, the aggregate data like the number of cardiologists using OpenEvidence might be. In September 2025, OpenEvidence bought an artificial intelligence advertising startup company, Amaro which specialized in “end-to-end advertising optimization using intelligent automation. Its technology is designed to help companies streamline deployment and maximize performance across digital channels.” So, it wouldn’t be a stretch to envision that OpenEvidence could say to a potential advertiser that they have X number of cardiologists, Y number of oncologists, Z number of endocrinologists, etc. and can offer targeted advertising to those users in those specialties. So a pharma company could have their statin drugs show up whenever a cardiologist does a search, oncologists would see their immunotherapy drugs, and insulin ads would show for endocrinologists when they search. So, the advertising would be agnostic of the search and information retrieved but would follow user profiles to target advertising.
This type of advertising in medicine isn’t new. Back in the day (I feel old saying that) when scientific journals were in print, not online. The publishers had the aggregated data of their subscribers and sold print advertising in their journals much the same way. It would not be unusual for advertising for drugs or other medical devices to be on printed on the back of research articles within the journal. Publishers would know their subscribers and would place ads directed to cardiologists in the journal that was sent to a cardiologists home or office. Advertisements for other products would go into journals sent to peoples’ homes or offices based on their discipline.
So now we know how OpenEvidence gets its money, or how it intends to. What makes it so great? Short answer…I don’t know. I am not a doctor or nurse. I don’t treat patients. So I don’t have an NPI to register for an account. I am uncertain of their rationale lock it to only a person with an NPI. Whether it is OpenEvidence’s myopic view of a “healthcare professional” or strategic opacity to maintain a competitive edge (which isn’t an unfounded concern: see how competitor impersonated physicians to access & hack OpenEvidence), that drives the NPI access requirement. But those of us who are involved in medical information and research (such as medical librarians) don’t have an NPI and can’t test or evaluate the product.
The Forbes article is full of glowing reviews from individual doctors detailing how much time it has saved them or pointed them to areas where they could expand their research. A quick and dirty search on PubMed for OpenEvidence yielded 18 citations (as of November 6, 2025), some of which review it or compare it to other products for clinical care.
The subject matter definitely “mattered” when it came to how well OpenEvidence performed. Apparently, for evaluating structural heart defects, ChatGPT performed better. (Struct Heart 2025 Jul 5;9(9):100696. doi: 10.1016/j.shj.2025.100696) But when it came to searching for the National Comprehensive Cancer Network (NCCN) guidelines for basal cell carcinoma and squamous cell carcinoma (SCC), OpenEvidence “scored significantly” better than ChatGPT. (Int J Dermatol 2025 Jun 5. doi: 10.11/ijd.1783)
Several studies evaluating OpenEvidence alone or against other resources such as ChatGPT, other AI programs, or UpToDate were somewhat limited in my opinion. One study, disappointedly only looked at 5 cases….really is that a true test? Others looked at the use of OpenEvidence for patient education material or discharge material. While OpenEvidence does provide the ability for physicians to “Write a Patient Handout,” I question whether a program that has medical information intended for clinicians is able to generate appropriate patient education material at a suitable reading level. (The AMA recommends patient education material should be at 5th-6th grade reading level.)
Lack of search precision – inability to search for specific articles, authors or journals which is extremely important…especially if you know of a specific study or article on the topic.
Opaque curation – OpenEvidence is not clear about its article selection and ranking. This is important as medicine needs to be very clear about the evaluation of evidence.
Limited interface – It lacks the ability to clarify queries or work interactively like programs such as ChatGPT.
Inconsistent Evidence – For example, authors said it “identified buspirone usage in OCD but missed additional supporting studies, limiting scope and confidence.”
As a medical librarian, I share those same concerns. I am especially concerned about the evidence within OpenEvidence, ability to access the source material, and a lack of medical professionals and information professionals on the OpenEvidence Team.
Where and what material are they using? We know that they have content from NEJM and JAMA, but what other quality resources do they have access to? Are they able to get behind the firewall of publishers’ content to provide information.
What ability do clinicians have to access the source material? Users cannot access their institutional holdings by connecting OpenEvidence to an institutional authentication system to access the full text of the journal articles. I have talked to a lot of smart computer people who think they just need to include the DOI so people can get the article. They are very smart computer people but very unaware of how hospitals, medical schools, and libraries license journals for medical professionals to read. *Note I did field a question from a doctor using OpenEvidence about adding OpenAthens (an authentications system) to access the full text of journals. But we use a different authentication system so I was unable to help him or even verify if using OpenAthens is even possible.
Of the 26 people listed on OpenEvidence’s team, only one has an MD. The rest? A ton of computer scientists, mathematicians, and statisticians. Smart people, no doubt, but wouldn’t you want more than one medical professional curating clinical evidence? It’s deeply concerning that a product claiming to revolutionize medical decision-making has so few actual medical professionals on the Team. And let’s not even talk about the complete absence of information professionals (you know, the people trained to evaluate, organize, and retrieve medical literature).
But, I get it. Medical librarians are kind of like the power company: nobody thinks about us until the lights go out…or until they need an obscure case report, or full-text article buried behind a paywall and their “Google it” approach fails. Still, if you’re building a tool to sift through clinical evidence, maybe – just maybe- you should include the people who specialize in doing exactly that. The omission feels less like an oversight and more like a typical Tech Bro blind spot: build fast, break things, and forget the people who’ve been quietly keeping the lights on in medical research for decades.
So can I recommend OpenEvidence? I don’t know…and that’s exactly the problem. It’s the latest AI-powered darling of medicine, launched by Harvard-affiliated founders and backed by $210M in funding. It’s free for verified U.S. physicians and medical professionals with an NPI, monetized through advertising, and praised for saving doctors time. But as a medical librarian, someone trained in evidence evaluation and information retrieval, I’m locked out. No NPI, no access. That means I can’t assess its sources, search precision, transparency, or even help clinicians connect it to the full text of the citations. When the very people who specialize in evaluating medical information are excluded, it raises concerns. Until more voices from the information side of healthcare are included and kick the AI’s tires, it’s hard to fully know if OpenEvidence is smart medicine….or just smart marketing?
*I used AI to aid in my research and writing of this post
I have been meaning to write a post about this article in TechCrunch, “Researchers seek to influence peer review with hidden AI prompts” but time slipped by faster than I anticipated. However, I still feel it is an important thing to post about; if anything to open people’s eyes to how unscrupulous authors have found a way to game the system.
According to the article, researchers have been caught embedding hidden prompts in preprint manuscripts to influence AI-assisted peer review. Often this was done using white text or teeny tiny fonts that the human eye doesn’t see or notice but the AI can “see.” These white or tiny font prompts instruct AI tools to deliver positive feedback, praising papers for their “impactful contributions” and “methodological rigor.”
The TechCrunch article mentions a report by Nikkei Asia, where at least 17 papers on arXiv contained such embedded instructions. The authors of these hidden AI message papers hailed from 14 institutions across eight countries, including places in the U.S. such as Columbia University and the University of Washington.
The Nikkei Asia report interviewed a professor who said this practice was to “counter against ‘lazy reviewers’ who use AI.” IMHO I find it ironic that the professor is choosing what seems to be a lazy way to counter “lazy reviewers.” If they really wanted to counter lazy reviewers perhaps embedding a hidden message to tell the reviewer using AI to stop using AI to review the paper. But alas they chose a way to make sure that the lazy reviewer using AI was still giving great reviews, so not really combating the problem. (Again IMHO it sounds more like someone who caught got with their hand in the cookie jar.)
So why is this more than a reviewer problem?
How many researchers use AIs like ResearchRabbit, Elicit, Undermind, etc. to find articles and research from preprint servers like medRxiv and bioRxiv that have yet to be published in the journal literature? While ResearchRabbit and Undermind do not directly use data from medRxiv and bioRxiv, they draw their content from Semantic Scholar….which in turn gets content from medRxiv and bioRxiv.
I am not picking on medRxiv or bioRxiv, really it can happen to any preprint archive/database that allows the article to be uploaded as text or other format that would allow an unscrupulous author to add AI positive review instructions. This type of AI trick theoretically would not work for accepted and published articles, because the formatting it would go through to be published would typically strip the code/text. That doesn’t mean it would be detected before acceptance…the publisher would have to be on the look out for that. It just means the person utilizing an AI program to find research published in journals typically wouldn’t fall prey to AI review trickery.
But a lot of people use those tools to do research and the creators of those tools highlight the benefits of their AI finding preprints. If those tools are ingesting content from platforms like Semantic Scholar, which in turn aggregates from medRxiv, bioRxiv, arXiv, and others preprint servers, then the potential for AI manipulation extends far beyond peer review.
So it is no longer just a reviewer problem. It’s a discovery problem, a credibility problem, and ultimately a trust problem.
As a medical librarian, I find this deeply concerning. And so should anyone in the biomedical community dedicated to evidence-based practice, source transparency, and rigorous peer review. If AI tools used by clinicians, researchers, and students can be manipulated by unethical authors, then there are huge risks that many are unaware of.
What can be done? Stop using AI? No. But, AI developers need to build in safeguards against prompt injection and hidden formatting. Publishers need to scan for manipulation before and after acceptance. Librarians and educators need to raise awareness and advocate for responsible AI use.
AI isn’t going away so we must ensure that the tools we use to find and evaluate research aren’t susceptible to this type of fraud and can be tweaked to prevent it.
I love technology. I love librarianship. I love the intersection of the two—where innovation meets information, and where we, as librarians, help people navigate the ever-evolving landscape of knowledge. But lately, I’m tired. Tired of what feels like the AI wars.
We’re in a moment of rapid transformation, and while I know it’s essential to stay current with advances in AI and information retrieval, the pace and fragmentation of it all is exhausting. Every week seems to bring a new tool, a new policy, a new platform—each promising to revolutionize research, while simultaneously complicating the very ecosystem we’ve spent decades building.
The Fragmentation of AI Tools
Let’s start with the publisher-created AIs. These tools are often limited to the publisher’s own content, creating silos that lack the diversity and breadth of scholarship needed for real, comprehensive research. They’re polished, yes—but narrow. They reinforce the walled information gardens we’ve been trying to break down in libraries for years.
Then there are the LLM-powered platforms that promise to assist with literature reviews and synthesis. These tools are exciting, but they often hit paywalls. They can’t access the full range of scholarly content behind institutional subscriptions, and most don’t integrate with our link resolvers—the very systems designed to connect users to full-text content we’ve already paid for. So we end up with tools that are powerful in theory but incomplete in practice leaving our users disconnected from the information.
The Copyright Conundrum
Meanwhile, users—well-meaning and curious— seem to want to upload everything into the AI of their choice. Library subscriptions, PDFs, paywalled articles, open access content… if it’s digital, they want to feed it into ChatGPT, Claude, Copilot or whatever tool they’re experimenting with. But copyright doesn’t disappear just because the interface is conversational. Additionally, any content created by AI is not subject to copyright – many users don’t realize this either.
We’re now spending more time educating users on copyright, fair use, and licensing than ever before. And it’s not just students—faculty and researchers are also navigating this new terrain, often unaware of the legal and ethical implications of how they use AI tools.
The Shifting Sands of Licensing
And just when we think we’ve got a handle on things, the publishers change the rules. Contracts that once allowed for text and data mining (TDM) are now being rewritten with restrictive AI clauses. These clauses often limit or eliminate our ability to mine content for research or innovation—something we’ve supported for years as part of open science and discovery.
Each publisher and company has their own unique rules regarding the licensing of AI (or inability to license AI). At MLA one librarian discussed trying to license a few journals (less than 100) to use with an institutional AI. Those journals had different publishers and different licensing requirements and fees, requiring hours/days/weeks of negotiating. This is impractical when dealing with the thousands of resources a library subscribes to. It feels like we’re being pushed out of the very conversation of connecting users to information that we helped start.
Wishing for the Fast Forward Button
Some days, I wish I could just hit the fast forward button—skip ahead to when the dust has settled, the standards are clearer, and the tools are interoperable. I want to get to the part where AI is a seamless part of the research process, not a battleground of competing interests, legal gray areas, and technological silos.
But I know that’s not how progress works. We’re in the messy middle. And as much as I’m tired, I also know this is where librarians are needed most.
We are the translators, the educators, the advocates. We understand metadata, licensing, access, and equity. We know how to ask the hard questions about bias, transparency, and sustainability. And we care—deeply—about helping people find, use, and trust information.
So yes, I’m tired. But I’m also still here. Still learning. Still advocating. Still believing that librarianship has a critical role to play in shaping the future of AI in research.
Let’s just hope that future gets here soon.
**1st Note** I want to be transparent that AI was used to aid in the creation of this post as I continue to attempt to learn ways to use AI better.
**2nd Note** My exhaustion is not regarding any specific company’s AI or type of AI, just tired of living in the messy middle.
In recent years, AI-powered tools like Grammarly, Quillbot, and Ginger Software have become increasingly popular for assisting with writing articles and papers. These tools offer a range of features, from grammar and spell checking to paraphrasing and style suggestions. However, their use also raises several concerns that writers and researchers should be aware of. Additionally, AI detection software like Turnitin and iThenticate plays a crucial role in maintaining academic integrity. Here, we explore the key concerns associated with using AI tools for writing and the potential pitfalls of AI hallucination in research, along with real-world examples of accusations faced by students and professionals.
1. Over-Reliance on AI Tools
One of the primary concerns with using AI tools like Grammarly, Quillbot, and Ginger Software is the risk of over-reliance. While these tools can significantly improve the quality of writing by catching errors and suggesting improvements, they can also lead to a dependency that may hinder the development of a writer’s own skills. Writers may become less attentive to their own mistakes and rely too heavily on AI to correct them, potentially stunting their growth as proficient writers.
2. Quality and Accuracy of Suggestions
AI tools are not infallible. The suggestions they provide may not always be accurate or contextually appropriate. For instance, Grammarly might flag a sentence as grammatically incorrect when it is, in fact, correct in the given context. Similarly, Quillbot’s paraphrasing might alter the original meaning of a sentence, leading to misinterpretation. Users must critically evaluate the suggestions provided by these tools and not accept them blindly.
3. Ethical Concerns and Plagiarism
The use of AI tools for paraphrasing, such as Quillbot, raises ethical concerns related to plagiarism. While these tools can help rephrase content to avoid direct copying, they can also be misused to produce work that is not genuinely original. This is where AI detection software like Turnitin and iThenticate becomes essential. These tools help identify instances of plagiarism and ensure that the work submitted is original and properly cited. However, the effectiveness of these detection tools depends on their ability to keep up with the evolving capabilities of AI writing tools.
4. Real-World Examples of Accusations
There have been instances where students and professionals have faced accusations of using AI to write their papers when they actually used tools like Grammarly, Quillbot, or Ginger Software. For example, Haishan Yang, a former Ph.D. student at the University of Minnesota, was expelled after being accused of using AI on a preliminary exam. Yang denied the allegations, stating that he used AI tools for various tasks but not on the test. Similarly, Marley Stevens, a student at the University of North Georgia, was accused of using AI in a paper. She claimed to have used Grammarly, as recommended by her school, but still faced repercussions that affected her GPA and scholarship
5. AI Hallucination in Research
AI hallucination refers to instances where AI generates information that is not based on actual data or facts. This is particularly concerning in the context of research, where accuracy and reliability are paramount. When using AI tools to assist with research, there is a risk that the AI might produce plausible-sounding but incorrect information. Researchers must be vigilant and cross-check any AI-generated content against credible sources to ensure its validity.
6. Unreliability and False Positives in AI Detection
Another concern with using AI writing tools is the privacy and security of the data inputted into these systems. Users often input sensitive information, including proprietary research data or personal details, into these tools. It is crucial to understand the data policies of these AI tools and ensure that the information is not being misused or stored without consent.
Conclusion
While AI tools like Grammarly, Quillbot, and Ginger Software offer valuable assistance in writing articles and papers, it is essential to be aware of their limitations and potential pitfalls. Over-reliance, accuracy of suggestions, ethical concerns, AI hallucination, and data security are critical issues that users must consider. Additionally, AI detection software like Turnitin and iThenticate plays a vital role in maintaining academic integrity by identifying plagiarism. By using these tools judiciously and critically evaluating their output, writers and researchers can harness the benefits of AI while mitigating the associated risks.
What are your thoughts on the use of AI in writing? Have you encountered any challenges or benefits that you’d like to share?
**Note** If you made it this far you should be aware this post was written by Copilot. I realize there is a bit of irony in that. But I am curious about a few things about how it did and about how it might get picked up by other bots.
BTW I had to fix all of the links to the statements. The first citation re: Yang was incorrectly attributed to this article where Yang’s name is not mentioned anywhere. Instead it found the information from this article, which actually is what inspired me for this post, but failed to cite it correctly.
I am excited share the podcast, “The Future of Medical Libraries: Evolving Roles in Research, Education and Clinical Support” where we talk about the rapidly evolving field of medical library science. No longer keepers of information, the librarian’s role has shifted to that of enablers of research and education in a digital age. Dr. Tizzano and I discuss trends such as artificial intelligence, hospital mergers and the challenges of funding and access in medical libraries. We also dive a little into the problems of fake medical information/news, the importance of systematic reviews and the critical support librarians provide to healthcare professionals.
Enjoy and please feel free to forward to anyone who may not realize the benefits of having a medical librarian in their institution.
Comment on: What are your thoughts? What other ways can medical librarians be involved in the future of healthcare, medicine, biomedical research? What are some barriers as well as opportunities to our involvement?
I have been a librarian for over 25 years (that means I am old). As someone who has been in a leadership position for a few of those years, one of the areas that concerns me are the challenges regarding educating and training the new generation of medical librarians to be ready for the current workforce and the future.
IMHO I believe ALA and many ALA accredited schools have failed us. They have been too slow to adapt to changes within the profession’s landscape. Students are graduating from programs with few skills or knowledge that would be considered applicable for even entry level positions. I have interviewed new graduates for medical librarian positions who have never heard of PubMed, UpToDate, Docline, CINAHL, etc. While these may be niche to the library world, they are some of the largest most heavily used resources in the medical library world. It is like interviewing for a public library position and saying you have never heard of Libby/OverDrive.
Even at entry level jobs you should have baseline knowledge for the professional position. While it is not impossible to get an entry level job without that baseline, it makes it much more difficult to do so. For example, it would be hard for me to get an entry level job as a computer programmer if I don’t have familiarity with any programming languages.
Many hospital libraries don’t have multiple librarians. First, you are lucky if the hospital has a library. If it has a library, and IF it has a medical librarian, there is usually only 1 person who is the medical librarian for that hospital. Hiring somebody and training them if they don’t even have the basics becomes challenging. The departing librarian must train the basics of medical librarianship PLUS the regular job training to the new hire in a matter of a few weeks before the departing librarian leaves. Of course that assumes the hospital allows for position overlap, meaning the new librarian is hired and trained by the departing librarian before they leave. Many places do not do position overlap. They hire after the person has left.
Academic medical libraries serving university and colleges with medical, nursing, and other health profession student typically have more than 1 librarian and the new employee can be trained and learn from other library staff. However, depending on the new hire’s pre-existing knowledge/skills and the job, it can take many months to train them.
The lack of knowledge and skills in health sciences librarianship in many graduates of library programs, makes it difficult to hire new library graduates.
However, as concerned as I am, I do have hope. Programs like WISE (Web-based Information Science Education) which allows students at ALA accredited WISE schools to take courses in special areas and from faculty at other WISE schools who are highly regarded in their area of expertise without the need or worry that credits will transfer. This sort of system makes sense. While there is a need to educate others in medical librarianship, the number of those interested in pursing that specialization is most likely not enough to support a program at SLIS if they only make it available enrolled students. But it still makes those specialized courses, necessary for those unique areas of librarianship available to students.
As a member of The Vision 2048 Task Force of MLA, I also have hope that the concerns and opportunities that we identified regarding educating future librarians will also be addressed by librarians, educators, library associations and accrediting bodies. The task force just published “Welcome to the Future: Challenges and Opportunities Discussed in The Vision 2048 Task Force Open Forums 2021-2023” (The main reason I decided to write this blog post) which identified education, pay, advocacy, as well as other things as important areas for the profession to address as we go forward.
While I still think I am a young 25 yr. old, I am actually closer to retirement than vanity likes me to admit. I care for this profession and hope that we can make adjustments so that when I retire (which is still awhile) the profession thrives and in the future there is a Vision 2063 addressing all new concerns and opportunities.
(Note: This is not a part of my AI writing experiments as some of my posts are. I will always note when I use AI on this blog.)
A post on Retraction Watch details an incident where Ina Vandebroek, the author of a book chapter “Ethical aspects of working with local communities and their biological resources,” in the 2017 edition of Pharmacognosy: Fundamentals, Applications and Strategies discovered the 2023 edition had the exact same chapter content from the 2017 edition (which she wrote) in the 2023 edition which she had declined to write or participate on. She stated the only differences are 2 additional sentences and the chapter listed different author.
Apparently, all you need to do be a chapter author in this Elsevier textbook is to add 2 sentences to what was written in the previous edition’s chapter for the new edition. Unfortunately, Elsevier’s contributor’s (author) agreement basically says that you if you publish a chapter in one of their books they may use all or any of the content for future editions.
While I understand Elsevier’s spirit of the contributor’s agreement, especially when you are talking about textbook chapters in book editions, this situation illustrates some concerns for authors, as it seems no meaningful new content was added and the original 2017 author’s words remained unchanged in any substantial way. So who is the real author, Vandebroek or the 2023 chapter author? Vandebroek sent a cease and desist letter claiming the book contains plagiarized material.
Readers also should have concerns as to what actually is written by the listed author and what was copy pasted from the previous edition? Vandebroek said that the entire chapter is exactly the same as what she wrote for the 2017 edition with the exception of 2 additional sentences. As I mentioned I don’t have either edition and I can’t find the references listed for that chapter in either the 2017 or 2023 edition online to compare to see if they are the same.
If references aren’t the same, and newer source material was referenced then how did the 2023 author do that while still using 2017 content? If the references are the same, then the 2023 author basically copy pasted the chapter and wrote 2 new sentences (as Vandebroek claims). Both of these situations are bad. You either have the “latest edition” of textbook that doesn’t have the latest information or a textbook that has fundamental flaws regarding writing research and references.
Unfortunately, it appears Vandebroek’s chapter is not the only chapter in this textbook where there are concerns with authorship. Retraction Watch details concerns regarding same 2023 author for Vandebroek’s chapter as being the sole author of 2 other chapters in that book, and other concerns regarding new authors for chapters attributed to different authors in the previous edition. Retraction Watch just mentions the author concerns. They did not go into any details as to whether those chapters had the same similarity problems as Vandebroek’s chapter.
It would be interesting if someone would look at the references for Vandebroek’s chapter in both the 2017 and 2023 editions to see whether they are the same. It would be also interesting to see if the other chapters of concern referenced by Retraction Watch suffered from the same cut and paste concerns as Vandebroek’ chapter has. IF you have both editions and have some spare time on your hands and want to report out on this, go for it, I would be interested in knowing.
Bing Chat is public large-language model (LLM), that is integrated with ChatGPT 4, is hosted by Microsoft, and is best used on the Microsoft Edge browser. The OpenAI and Microsoft relationship is confusing to me, but I thought I would play with Bing Chat anyway. (Here is an article and diagram on their confusing partnership)
There are no restrictions on date. If it was online today, it can be found
Lots of plugins, including OpenTable and Wolfram Alpha, including Kayak, Klarna, Redfin, and Zillow. BTW I have no idea why/how Bing Chat would be in OpenTable etc. But the article did mention travel queries so I would assume that is where Kayak comes into play.
Bing Chat is also in Windows 11 through a new Windows Copilot integration
You can use it for images
According Microsoft “the new Bing is faster, more accurate, and “more capable” than ChatGPT or GPT-3.5, the LLM behind ChatGPT.” Bing Chat uses ChatGPT 4.0 which is the only way you can use ChatGPT 4.0 for free.
So I did a little playing around. Please note Bing Chat responses are screenshot images. I found this made it a little easier to show the output style and also more clearly define my output vs Bing Chat’s output.
I decided to test 3 things: Citations, dates (timeliness) and for fun I wanted to test images.
Citations, Timeliness, and Real World Examples:
Prompt: Write 2 paragraphs on the use of ChatGPT in medical libraries and provide exampes of ChatGPT use in medicine with URLs to those examples.
Bing Chat’s response:
Ok it is a little weird to see my blog as the first thing listed “for more insights.” But the links did work when I clicked on all of them.
Let’s try some medical stuff. Prompt: Find 3 articles and provide citations on exercise induced asthma.
Bing Chat’s response:
Again the links worked when I clicked on them, including the one to UTD. But I was interested in getting some articles in PubMed so I refined my prompt.
Prompt: Search PubMed for 3 citations on exercised induced asthma, provide the PMID and a link to the article.
Bing Chat’s response:
The PMID’s were valid and the links to the articles went to the website where you could get the full text if you had a subscription.
PubMed Results vs Bing Chat:
I was interested in how Bing Chat’s results were different from a quick search on PubMed. Below are 2 screen shots from 2 searches. The first is exercise induced asthma searched as a keyword in PubMed. The second is exercise induced asthma searched as a MeSH term. Both searches were set to display Best Match. Since the articles Bing Chat retrieved clearly weren’t in most recent order, I was curious if Bing would retrieve things within PubMed more closely aligned with Best Match.
I have no idea why Bing Chat chose those 3 citations from PubMed, none of those citations were the most recent nor were they listed in the 50 citations displayed on PubMed’s Best Match results. – Weird
Images:
So what can Bing Chat do with images? I did some playing around with dog images. Note I uploaded an image of my dog, Bear. He is a Mountain Cur mix… so that is a little unfair that I did not upload a picture of a pure bred dog.
Bear with his favorite toy.
Prompt: What breed does this dog resemeble?
Bing Chat’s Response:
Wow that was not even close and the photo below Bing Chat’s response is certainly not a Golden Retriever.
Prompt: Show me a picture of a Mountain Cur mix dog
Bing Chat’s Response:
There we go… more like it. Especially the dog in the middle looks a lot like my pup.
Clearly Bing Chat had difficulty identifying the image I supplied but when I knew what type of image I wanted to retrieve, it did a good job.
Medical Images:
I don’t have any medical pictures that are copyright free AND aren’t already on the Internet where Bing Chat would be able to pull some sort of metadata on. So I didn’t test its capabilities of identifying medical images I supplied.
But I did ask it to retrieve pictures of asthmatic lungs. Yes that is very broad, that was intentional since I wanted to compare Bing Chat’s image retrieval to that of Google Images.
Prompt: Show me pictures of asthmatic lungs.
Bing Chat’s Response:
Google Images:
Obviously Google Images has a lot more images. I think Bing Chat is limited by its output style. However, it was interesting to see that the 3 images Bing Chat chose were not anywhere on the first page of results for Google Images. However, they were on the first page of Bing’s Images page (similar to Google Images). So clearly Bing Chat’s AI favor’s Bing search results over Google’s. (makes sense)
What was really weird and a little concerning was when I clicked on an image from Bing Chat, it was a little difficult to see where the image was from AND Bing Chat launched another tab on my browser to Bing Images and the top part of the page for that tab listed asthma medications that were for sale.
Screen shot: when I clicked on an image from Bing Chat. Note the list of websites on the right…where is this image from? Not real obvious. Note behind the giant black square the image is featured on, you can just see pill bottle sticking out on the left, that is the new tab that was opened on my browser.
Screen shot: Here is a screen shot after I closed the Bing Chat image. The newly opened tab is shows asthma medicine for sale above asthma lung images.
It is important to note if you search Bing for images (like you would Google Images) and don’t use Bing Chat to find images, you don’t get drugs listed for sale at the top (as seen in this screen shot below).
Bottom Line:
I think Bing Chat does a better job searching and finding text type of information. The links went to the correct places, I did not detect any hallucinations like I did with ChatGPT 3. I am still not sure why it chose the PubMed citations it did. Why were those better to show than a citation from one of PubMed’s 50 Best Match?
I am still sticking with PubMed for searching. But I am at least happy that it is actually retrieving the correct stuff.
I think there are a lot of concerns regarding it interpreting images, finding images, clearly showing the source of the images, and I am still irritated or unnerved that it opened a new tab on my browser featuring asthma drugs so prominently.
What interests you? What would like to see me try and test and play with next? Let me know by commenting or emailing me your suggestions.
(Michelle’s words) I know I said I would write a post once a month, but I wanted to do an update since several comments generated some interesting questions and ideas. Specifically, someone asked if the ChatGPT response could provide links to the examples in its post. I was intrigued and I wanted to see if the links suffered from AI hallucination like ChatGPT’s reference.
Here are the results:
First time ever (screen shot below) ChatGPT3.5 gave me two side by side answers and asked which one I liked better. My guess is to help it to learn.
Second, the answer differs some from the answer given in my original post. This is not unusual. You can ask it the same thing with the exact same wording and it will provide a different result each time.
HOWEVER, my prompt to ChatGPT was different than the one I posed for my original post because I asked it to provide links to examples. So naturally it will be different. ChatGPT does not take its original answer and tweak it, like adding links. It often creates a new answer
The links (copy paste snippet from CHATGPT3.5 below image) go nowhere. I clicked on them within ChatGPT and I got a 404 error. So the AI provides links but the links do not go to any real sites.
So I asked the question to ChatGPT as second time to see what changes would happen.
I didn’t get the side by side 2 answers…weird.
Content was different (as expected)
Links changed and work…sort of. They go to actual websites and I don’t get a 404 error but as you read they really are sucky examples. I want the URLs for the 1st try Response 2 to work, not links to regular websites that are involved in AI.
So I tried for a final 3rd time to get the results I wanted. It is Halloween, I have candy to handout and beers to drink….no 4th try.
***PLEASE NOTE*** getting the results you want is as much a part of what you write in the prompt as it is ChatGPT interpreting what you wrote. ***I can’t stress this enought***
So here was my 3rd try prompt:
write a short blog post on the use of ChatGPT in medical libraries and provide examples of ChatGPT use in medicine with URLs to those examples
My Results:
It did better on the linking. All but MedGPT’s link worked and went to real pages.
Examples were meh and possibly not relevant. For example, it listed UpToDate and how it could benefit from ChatGPT. That was not what I wanted, I wanted specific examples of it use not what can benefit from its use. However…. ChatGPT interpreted my prompt differently.
Please note, it did not provide linked examples for medical libraries, because I messed up in my prompt and wrote, “and provide examples of ChatGPT use in medicinewith URLs,” so it gave me exactly that.
(Screen shot of ChatGPT answer with 2 responses side by side)
(1st try Snippet of ChatGPT Response 2 words copy pasted: note links do not go anywhere)
Examples of ChatGPT in Action
To witness the transformative potential of ChatGPT in healthcare, here are some examples:
These examples illustrate the tangible benefits of combining ChatGPT with medical libraries, ultimately advancing patient care, research, and healthcare accessibility.
Conclusion
The partnership between ChatGPT and medical libraries is poised to reshape the healthcare landscape. By harnessing the power of AI-driven language models, healthcare professionals, researchers, and patients can access, interpret, and utilize medical knowledge more efficiently and effectively than ever before. As this collaboration continues to evolve, we can anticipate even more innovative applications that will elevate the quality and inclusivity of healthcare services globally.
(2nd try snippet of ChatGPT… links work but don’t add value IMHO)
Conclusion
The integration of ChatGPT with medical libraries holds immense promise in revolutionizing the healthcare information landscape. By simplifying information retrieval, offering contextual recommendations, and summarizing complex research, ChatGPT empowers healthcare professionals, researchers, and students to make informed decisions and advance medical knowledge. The combination of AI and medical libraries represents a significant step forward in the quest for better healthcare and medical breakthroughs.
Links to Examples:
Demo of ChatGPT: Explore the capabilities of ChatGPT in natural language understanding and generation.
PubMed Central: A well-known medical library where ChatGPT integration could enhance information access and retrieval.
ArXiv: A repository for academic papers, including medical research, which could benefit from AI-powered search and summarization features.
Google Scholar: A popular tool for academic research, which could potentially integrate ChatGPT for improved search and recommendations.
(Snippet from my 3rd and final try with ChatGPT and linking)
ChatGPT can be employed to summarize lengthy research papers or clinical studies, helping healthcare professionals and researchers quickly extract the key findings and implications from complex texts.
AI-powered systems like AIDoc use ChatGPT to assist radiologists and doctors in interpreting medical images and answering specific questions related to patient cases, potentially leading to faster and more accurate diagnoses.
IBM Watson for Oncology employs ChatGPT-like models to provide personalized treatment recommendations by analyzing a patient’s medical history and comparing it with an extensive database of medical literature.
Platforms like UpToDate can benefit from ChatGPT’s natural language search capabilities, allowing healthcare professionals to find relevant clinical information quickly and efficiently.
Benefits of ChatGPT in Medical Libraries
The integration of ChatGPT into medical libraries offers several significant advantages:
Simplified Search and Retrieval: ChatGPT enables users to search for medical information using natural language queries, reducing the need for complex search syntax.
Contextual Insights: ChatGPT can provide context-specific recommendations and insights by understanding the user’s inquiries.
Multilingual Support: ChatGPT can assist users in multiple languages, making medical literature more accessible to a global audience.
Quick Summarization: ChatGPT can summarize extensive medical documents, making it easier for healthcare professionals to extract pertinent information rapidly.
(Michelle’s words)
Keep the comments coming. I promise due to work life balance I am really only going to try and publish once a month. I swear…I think…I will try really really hard.
