Let's Chat About Chatbots: Additional Thoughts on ChatGPT and Its Role in Plastic Surgery Along With Its Ability to Perform Systematic Reviews.
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ABSTRACT:
SUBMITTER: Najafali D
PROVIDER: S-EPMC10264225 | biostudies-literature | 2023 Jun
REPOSITORIES: biostudies-literature
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Let's Chat About Chatbots: Additional Thoughts on ChatGPT and Its Role in Plastic Surgery Along With Its Ability to Perform Systematic Reviews.
Najafali Daniel D Reiche Erik E Camacho Justin M JM Morrison Shane D SD Dorafshar Amir H AH
Aesthetic surgery journal 20230601 7
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| S-EPMC10349644 | biostudies-literature
Project description:IntroductionPatients who stay in the emergency department (ED) for prolonged periods of time require verification of home medications, a process known as medication reconciliation. The complex nature of medication reconciliation can lead to adverse events and staff dissatisfaction. A multidisciplinary team was formed to improve accuracy, timing, and staff satisfaction with the medication reconciliation process.MethodsBetween November 2021-January 2022, stakeholders were surveyed to identify gaps in the medication reconciliation process. This project implemented education on role-specific tasks, as well as a "Let's chat!" huddle, bringing together the entire care team to perform medication reconciliation. We used real-time evaluations by frontline staff to evaluate effectiveness during plan- do-study-act cycles and obtain feedback. Following the implementation period, stakeholders completed the post-intervention survey between June-July 2022, using a 4-point Likert scale (0 = very dissatisfied to 3 = very satisfied). We calculated the change in staff satisfaction from pre-intervention to post-intervention. Differences in proportions and 95% confidence intervals are reported. This study adhered to the Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) and followed the Lean Six Sigma rapid cycle process improvement (define-measure-analyze-improve-control).ResultsA total of 111 front-line ED staff (physicians, nurse practitioners, physician assistants, pharmacists, nurses) completed the pre-intervention survey (of 350 ED staff, corresponding to a 31.7% response rate), and 89 stakeholders completed the post-intervention survey (a 25.4% response rate). Subjective feedback from staff identifying causes of low satisfaction with the initial process included the following: complexity of process; unclear delineation of staff roles; time burden to completion; high patient volume; and lack of standardized communication of task completion. Overall satisfaction improved after the intervention. The greatest improvement was seen in the correct medication (difference 20.7%, confidence interval [CI] 6.3-33.9%, P < 0.01), correct dose (25.6%, CI 11.4-38.6%, P < 0.001) and time last taken (24.5%, CI 11.4-37.0%, P < 0.001).ConclusionThere is a steep learning curve to educate multidisciplinary staff on a new process and implement the associated changes. With goals to impact the safety of our patients and reduce negative outcomes, engagement and awareness of the team involved in the medication reconciliation process is critical to improve staff satisfaction.
| S-EPMC11254147 | biostudies-literature
Project description:Humans are motivated to interact with each other, but the neural bases of social motivation have been predominantly examined in non-interactive contexts. Understanding real-world social motivation is of special importance during middle childhood (ages 8-12), a period when social skills improve, social networks grow, and social brain networks specialize. To assess interactive social motivation, the current study used a novel fMRI paradigm in which children believed they were chatting with a peer. The design targeted two phases of interaction: (1) Initiation, in which children engaged in a social bid via sharing a like or hobby, and (2) Reply, in which children received either an engaged ("Me too") or non-engaged ("I'm away") reply from the peer. On control trials, children were told that their answers were not shared and that they would receive either engaged ("Matched") or non-engaged ("Disconnected") replies from the computer. Results indicated that during Initiation and Reply, key components of reward circuitry (e.g., ventral striatum) were more active for the peer than the computer trials. In addition, during Reply, social cognitive regions were more activated by the peer, and this social cognitive specialization increased with age. Finally, the effect of engagement type on reward circuitry activation was larger for social than non-social trials, indicating developmental sensitivity to social contingency. These findings demonstrate that both reward and social cognitive brain systems support real-time social interaction in middle childhood. An interactive approach to understanding social reward has implications for clinical disorders, where social motivation is more affected in real-world contexts.
| S-EPMC7060940 | biostudies-literature
Project description:ObjectivesThis study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those of dental students on an oral and maxillofacial radiology examination.MethodsChatGPT, ChatGPT Plus, Bard, and Bing Chat were tested on 52 questions from regular dental college examinations. These questions were categorized into three educational content areas: basic knowledge, imaging and equipment, and image interpretation. They were also classified as multiple-choice questions (MCQs) and short-answer questions (SAQs). The accuracy rates of the chatbots were compared with the performance of students, and further analysis was conducted based on the educational content and question type.ResultsThe students' overall accuracy rate was 81.2%, while that of the chatbots varied: 50.0% for ChatGPT, 65.4% for ChatGPT Plus, 50.0% for Bard, and 63.5% for Bing Chat. ChatGPT Plus achieved a higher accuracy rate for basic knowledge than the students (93.8% vs. 78.7%). However, all chatbots performed poorly in image interpretation, with accuracy rates below 35.0%. All chatbots scored less than 60.0% on MCQs, but performed better on SAQs.ConclusionsThe performance of chatbots in oral and maxillofacial radiology was unsatisfactory. Further training using specific, relevant data derived solely from reliable sources is required. Additionally, the validity of these chatbots' responses must be meticulously verified.
| S-EPMC11358622 | biostudies-literature
Project description:Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.
| S-EPMC10605499 | biostudies-literature
Project description:BackgroundLarge language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of the information provided to users.ObjectiveThis study aims to examine whether 3 ChatGPT chatbots-the World Health Organization's Sarah, BeFreeGPT, and BasicGPT-provide reliable information on how to quit smoking.MethodsA list of quit smoking queries was generated from frequent quit smoking searches on Google related to "how to quit smoking" (n=12). Each query was given to each chatbot, and responses were analyzed for their adherence to an index developed from the US Preventive Services Task Force public health guidelines for quitting smoking and counseling principles. Responses were independently coded by 2 reviewers, and differences were resolved by a third coder.ResultsAcross chatbots and queries, on average, chatbot responses were rated as being adherent to 57.1% of the items on the adherence index. Sarah's adherence (72.2%) was significantly higher than BeFreeGPT (50%) and BasicGPT (47.8%; P<.001). The majority of chatbot responses had clear language (97.3%) and included a recommendation to seek out professional counseling (80.3%). About half of the responses included the recommendation to consider using nicotine replacement therapy (52.7%), the recommendation to seek out social support from friends and family (55.6%), and information on how to deal with cravings when quitting smoking (44.4%). The least common was information about considering the use of non-nicotine replacement therapy prescription drugs (14.1%). Finally, some types of misinformation were present in 22% of responses. Specific queries that were most challenging for the chatbots included queries on "how to quit smoking cold turkey," "...with vapes," "...with gummies," "...with a necklace," and "...with hypnosis." All chatbots showed resilience to adversarial attacks that were intended to derail the conversation.ConclusionsLLM chatbots varied in their adherence to quit-smoking guidelines and counseling principles. While chatbots reliably provided some types of information, they omitted other types, as well as occasionally provided misinformation, especially for queries about less evidence-based methods of quitting. LLM chatbot instructions can be revised to compensate for these weaknesses.
| S-EPMC11826940 | biostudies-literature
Project description:On November 30, 2022, OpenAI enabled public access to ChatGPT, a next-generation artificial intelligence with a highly sophisticated ability to write, solve coding issues, and answer questions. This communication draws attention to the prospect that ChatGPT and its successors will become important virtual assistants to patients and health-care providers. In our assessments, ranging from answering basic fact-based questions to responding to complex clinical questions, ChatGPT demonstrated a remarkable ability to formulate interpretable responses, which appeared to minimize the likelihood of alarm compared with Google's feature snippet. Arguably, the ChatGPT use case presents an urgent need for regulators and health-care professionals to be involved in developing standards for minimum quality and to raise patient awareness of current limitations of emerging artificial intelligence assistants. This commentary aims to raise awareness at the tipping point of a paradigm shift.
| S-EPMC10013638 | biostudies-literature
Project description:The birth of ChatGPT, a cutting-edge language model-based chatbot developed by OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role in rigorous scientific research is not clear yet. This paper vividly showcases its innovative application within the field of drug discovery. Focused specifically on developing anti-cocaine addiction drugs, the study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates. The primary objective is to generate optimal drug-like molecules with desired properties. By leveraging the capabilities of ChatGPT, the study introduces a novel approach to the drug discovery process. This symbiotic partnership between AI and researchers transforms how drug development is approached. Chatbots become facilitators, steering researchers towards innovative methodologies and productive paths for creating effective drug candidates. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the design and development of potential pharmaceutical solutions. This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.
| S-EPMC10462169 | biostudies-literature
Project description:Joint engagement with a speaker is one cue children may use to establish that an interaction is relevant to them and worthy of attention. People on pre-recorded video cannot engage contingently with a viewer in shared experiences, possibly leading to deficits in learning from video relative to learning from responsive face-to-face encounters. One hundred and thirty two toddlers (24 and 30 months old) were offered referential social cues disambiguating a novel word's meaning in one of four conditions: responsive live (a speaker was present and engaged with children); unresponsive video (a speaker on video looked at the camera and smiled at scripted times); unresponsive live (although present, the speaker behaved as she did on the unresponsive video), and responsive video (a speaker on closed-circuit video engaged with children, as in video chat). Children of both ages reliably learned the word in the responsive live condition, and older children (30 months) learned in the unresponsive live condition. Neither group learned in the responsive or unresponsive video conditions. The results show that the addition of communicative social cues to the video presentation via video chat was not sufficient to support learning in this case. Rather, toddlers' transfer and generalization of words presented on video chat may depend on other contextual factors, such as co-viewers who scaffold their learning. Live, responsive video as implemented in this and prior studies is compared, with implications for the use of video chat via the Internet with young children.
| S-EPMC6243085 | biostudies-literature
Project description:BackgroundValuable findings can be obtained through data mining in patients' online reviews. Also identifying healthcare needs from the patient's perspective can more accurately improve the quality of care and the experience of the visit. Thereby avoiding unnecessary waste of health care resources. The large language model (LLM) can be a promising tool due to research that demonstrates its outstanding performance and potential in directions such as data mining, healthcare management, and more.ObjectiveWe aim to propose a methodology to address this problem, specifically, the recent breakthrough of LLM can be leveraged for effectively understanding healthcare needs from patient experience reviews.MethodsWe used 504,198 reviews collected from a large online medical platform, haodf.com. We used the reviews to create Aspect Based Sentiment Analysis (ABSA) templates, which categorized patient reviews into three categories, reflecting the areas of concern of patients. With the introduction of thought chains, we embedded ABSA templates into the prompts for ChatGPT, which was then used to identify patient needs.ResultsOur method has a weighted total precision of 0.944, which was outstanding compared to the direct narrative tasks in ChatGPT-4o, which have a weighted total precision of 0.890. Weighted total recall and F1 scores also reached 0.884 and 0.912 respectively, surpassing the 0.802 and 0.843 scores for "direct narratives in ChatGPT." Finally, the accuracy of the three sampling methods was 91.8%, 91.7%, and 91.2%, with an average accuracy of over 91.5%.ConclusionsCombining ChatGPT with ABSA templates can achieve satisfactory results in analyzing patient reviews. As our work applies to other LLMs, we shed light on understanding the demands of patients and health consumers with novel models, which can contribute to the agenda of enhancing patient experience and better healthcare resource allocations effectively.
| S-EPMC11918364 | biostudies-literature