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.