Is the Future of Medicine Just a Prompt Away?

In Evaluating General-Purpose LLMs for Patient-Facing Use: Dermatology-Centered Systematic Review and Meta-Analysis (medRxiv, 2025), the data tells a fascinating story: large language models (LLMs) are improving in medical reasoning, empathy, and safety - but they’re not perfect, and trust takes time to earn. Which, come to think of it, sounds a lot like the long human history of hoping for miracle healers.

Long before stethoscopes, scalpels, and sterile gloves, our first “doctors” were magicians - or at least, that’s what everyone believed. Prehistoric healers waved bones, mumbled incantations, and applied sometimes questionable herbal pastes. Yet enough patients recovered to keep the legend alive.

Fast forward a few millennia and not much has changed… except the props. The bone rattle has been replaced by a diagnostic app. The “spirit-cleansing smoke” is now an MRI scan. And our new shamans? They’re called AI engineers.

Just like in the old days, we still crave the miracle cure, the instant fix, the all-knowing healer. Our dream is a tireless personal doctor who remembers every ache, every allergy, every bit of medical literature (plus the plot of every episode of Grey’s Anatomy).

When ChatGPT burst into the public spotlight in late 2022, some were fascinated and some were wary. Could a chatbot really diagnose a rash? Suggest a safe treatment? Explain it all in plain language?

Early studies, including those reviewed in the paper, painted a mixed picture. In 2022, the mood was skeptical. By 2023, optimism surged as newer models like GPT-4, Claude, and Gemini started showing measurable gains in accuracy, empathy, and communication. But by 2025, the mood had shifted again - not to cynicism, but toward a more critical view.

The truth is, AI in medicine is a lot like the magic of old: it works impressively well in certain contexts, but not always when or how you expect. LLMs are now better at interpreting images, offering solid medication safety advice, and even admitting when they don’t know - a kind of digital humility our ancestors probably wished their witch doctors had. But they still have limits. Even when an AI aces a medical board exam and offers great second opinions, patients using it alone don’t necessarily make better decisions.

That’s why the paper calls for evaluator-aware, patient-in-the-loop frameworks - ways of measuring not just whether the AI gets the right answer, but whether it helps real people make better choices. Because in healthcare, as in magic, the spell only works if it actually helps the patient in the real world.

REFERENCE

Irene S. Gabashvili Evaluating General-Purpose LLMs for Patient-Facing Use: Dermatology-Centered Systematic Review and Meta-Analysis medRxiv 2025.08.11.25333149; doi: https://doi.org/10.1101/2025.08.11.25333149

The Past, Present, and Future of AI-Powered Medicine

In this era of rapidly advancing technology, Artificial Intelligence (AI) is spearheading transformative changes, particularly within the healthcare sector. A prime example is ChatGPT, a game-changer that is fast becoming an influential player in the biomedical field. Its potential to catalyze innovation and revolutionize medical research is profound. 

A MedrXiv paper posted today is a systematic review of AI in biomedical literature. The scope of this review is broad, encompassing preprints, peer-reviewed articles, case reports, patents, clinical trials, and even FDA approvals. The paper talks about impact of AI, including ChatGPT, across all medical specialties and subsets of publications, finding overrepresented domains within each subset, highlighting potential research gaps, biases, or areas of excessive focus. 

The pace at which the biomedical literature is growing is staggering, with two new papers being added every minute, around the clock. The MedrXiv paper also brings to light the limitations of current medical publishing models. 

The human author collaborated with ChatGPT, Bing, Claude, and Bard, and used SciSpace Copilot to review hundreds of systematic reviews and thousands of unique records across various databases. It aimed to comprehensively map out past achievements, current developments, and potential future directions in AI-based medical research. The paper represents the latest brainchild of an open science collaboration project, which welcomes participation from others at https://osf.io/87u6q/. 

The study reveals that as ChatGPT matures, it's finding applications across diverse medical specialties demanding cross-disciplinary collaboration. A fascinating trend shows a transition from theoretical to clinical applications in the AI literature, mirroring developments within the ChatGPT space.

Despite challenges such as ensuring the quality of training data and managing ethical concerns, ChatGPT is fostering the adoption of AI tools within medical areas that have been historically underrepresented in AI application.

If you have an interest in the future of medicine and the role AI technologies like ChatGPT play in shaping it, check out this project. It offers a compelling glimpse into what the future of healthcare might look like. 

REFERENCES

Gabashvili I.S. Artificial Intelligence in Biomedicine: Systematic Review. medRxiv 2023.07.23.23292672; doi: https://doi.org/10.1101/2023.07.23.23292672

Gabashvili I.S. The impact and applications of ChatGPT: a systematic review of literature reviews. arXiv:2305.18086 [cs.CY] https://doi.org/10.48550/arXiv.2305.18086

Gabashvili IS. ChatGPT in Dermatology: A Comprehensive Systematic Review JMIR Preprints. 02/06/2023:48979. medRxiv 2023.06.11.23291252; https://doi.org/10.1101/2023.06.11.23291252

Transforming Dermatology

Artificial intelligence has witnessed a rapid surge in the adoption of language models, with transformer-based pretrained language models (T-PLMs) playing a prominent role. These T-PLMs, such as BioBERT, MEP & Bioformer (BERT), Chatdoctor (LLaMA), OPAL & MedDialogue (BART), BioGPT and MedGPT (GPT 2), have revolutionized natural language processing in the biomedical research community, offering tailored performance for specific applications. Among these models, ChatGPT stands out due to its versatility, flexibility, and accessibility, making it widely applicable across various domains, including dermatology.

The fact that ChatGPT may be used by everyone, as opposed to models that can only be used by specialized developers and researchers, is one of its main advantages. This accessibility has paved the way for new possibilities in the field. By integrating clinical knowledge with interactive conversation, new generation of language models has the potential to transform all biomedical fields.

A recently posted preprint, open to peer review till July 8, focuses on the utilization of ChatGPT in dermatology. However, the study's conclusions have implications beyond dermatology and are relevant to other biomedical disciplines as well. Unlike previous reviews, this study goes further than performance evaluation by analyzing the actual utilization and real-world applications of ChatGPT in dermatology-related areas. By examining its practical use, the study offers valuable insights into the potential impact of ChatGPT in the broader biomedical research community. 

REFERENCE: 

Gabashvili IS. ChatGPT in Dermatology: A Comprehensive Systematic Review JMIR Preprints. 02/06/2023:48979. medRxiv 2023.06.11.23291252; https://doi.org/10.1101/2023.06.11.23291252 

Accelerating Knowledge Innovation: Systematic Review of Reviews on ChatGPT

In the beginning was the Word. It was used in the creation of all other things - from thoughts to stories and histories. However, as the amount of information available grew overwhelming, it became challenging to process and make sense of it all. Early academic reformers introduced the idea of reviews and digests to help navigate this sea of information. But it wasn't until the 1970s that systematic reviews gained popularity, starting in the field of medical research.

created by Author with ChatGPT, Bing Image Creator & Photoshop

Reviews play a crucial role in consolidating a vast array of studies and publications, allowing researchers to weave together the threads of evidence and create a cohesive and informative narrative. However, traditional review processes often require substantial time and the collaboration of multiple researchers to reach a consensus. With the rise of generative AI based on large language models, the power of words can be harnessed to streamline the systematic review process, unlocking new possibilities for learning and knowledge acquisition. By leveraging the capabilities of ChatGPT, researchers can potentially accelerate the production of high-quality reviews, facilitating the dissemination of insights and advancements in various fields of study.

The use of ChatGPT in conducting a systematic review of reviews on ChatGPT itself demonstrates the potential for accelerating the production of high-quality reviews in a timely manner. 

While systematic reviews are considered to be the gold standard in knowledge synthesis, they usually require between 6 months and 2 years to complete and often have a narrow focus. While the methodological shortcuts allow rapid reviews (first mentioned in the literature in 1997) to be conducted in less time and with fewer resources, they also increase the likelihood of introducing bias into the review process and missing important information from grey literature (i.e., preprint servers). 

In 2020, full systematic review was completed by a team of 6 in 2 weeks using automation tools. The most time-consuming tasks were data extraction, write-up, abstract screening, full-text screening, and risk of bias. 4 out of the 6 people on the team were experienced systematic reviewers with complementary skills (three experts in two domains required for the review and one information specialist). 

In 2023, ChatGPT and I were able to complete the review of reviews in one week. We screened 7 large resources of papers, including grey literature and reviewed primary studies in Chinese, German, Indonesian, Norwegian, Portuguese, Russian, and Spanish, in addition to English. 

ChatGPT helped me to filter relevant literature in all languages, extract key information, summarize findings, and even assisted with the synthesis of the overall review, enabling a more efficient and comprehensive analysis.

Our paper illustrates that ChatGPT is expanding into different domains and highlights the need to continually refine and expand the training datasets, ensuring that they are diverse and accurate. Another area of improvement involves developing customized integrations, designing specialized prompt instructions and involvement of domain-specific expert trainers, factual correctness evaluation, and investigation of societal impact.  

Word by word, paper by paper, and review by review, ChatGPT is paving the way for a future where knowledge creation is accelerated, insights are amplified, and breakthroughs are within closer reach.

REFERENCE

Gabashvili I.S. The impact and applications of ChatGPT: a systematic review of literature reviews. Submitted on May 8, 2023. arXiv:2305.18086 [cs.CY]. https://doi.org/10.48550/arXiv.2305.18086

Depression AI

Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not yet ready for use in clinical practice - as concluded in a recent review. AI can be also used therapeutically. 

Research has shown that interacting with technology, such as chatbots, can lead to feelings of social connection and companionship, which can have both positive and negative effects on mental well-being. Chatbots have become increasingly popular in mental health domain because of their impact on social interactions and the ability to form and maintain meaningful relationships. They are effective in reducing symptoms of anxiety and depression, although there is always a risk that they may exacerbate mental health issues. 

One of the main benefits of chatbots is their ability to provide low-cost and easily accessible mental health counseling. ChatGPT studies show that its potential for depression detection and treatment should be further explored, while addressing the challenges and ethical considerations. ChatGPT outperforms traditional neural network methods but still has a significant gap with advanced task-specific methods. 

In the US, one in five individuals is affected by mental health issues each year, with recreational cannabis use increasing the risk. Intelligent wearables utilize over 30 types of data to predict depression, with physical activity, sleep, heart rate, and mental health measures being the most commonly used. The Depresjon dataset (motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls) is most popular among researchers.

Previous systematic reviews have shown that AI has better performance in detecting patients without depression than those with depression, but the review published last week shows slightly higher sensitivity and specificity - based on data from wearable devices. It is recommended that tech companies develop wearable devices that can detect and predict depression in real-time. Neuroimaging data in addition to wearable devices would provide even higher diagnostic performance.

With the increasing popularity of IoT and AI, they will likely become an integral part of our lives.  It may soon become a useful tool in clinical practice.

REFERENCES

Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. NPJ Digit Med. 2023 May 5;6(1):84. doi: 10.1038/s41746-023-00828-5. PMID: 37147384.

Garcia-Ceja E, Riegler M, Jakobsen P, Tørresen J, Nordgreen T, Oedegaard KJ, Fasmer OB. Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients. In Proceedings of the 9th ACM multimedia systems conference 2018 Jun 12 (pp. 472-477).

Lamichhane B. Evaluation of ChatGPT for NLP-based Mental Health Applications. arXiv preprint arXiv:2303.15727. 2023 Mar 28. 

Yang K, Ji S, Zhang T, Xie Q, Ananiadou S. On the Evaluations of ChatGPT and Emotion-enhanced Prompting for Mental Health Analysis. arXiv preprint arXiv:2304.03347. 2023 Apr 6.

Dana RA, Gavril RA. Exploring the psychological implications of ChatGPT: a qualitative study. Journal Plus Education. 2023 May 1;32(1):43-55.