Limitations Of Artificial Intelligence Research Articles

Artificial intelligence in epilepsy - applications and pathways to the clinic.

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

[ScreenGPT - The opportunities and limitations of artificial intelligence in primary, secondary and tertiary prevention].

[ScreenGPT - The opportunities and limitations of artificial intelligence in primary, secondary and tertiary prevention].

Leveraging Artificial Intelligence (AI) As a Critical Friend: The Affordances and Limitations

ABSTRACT This qualitative self-study contributes new insights on critical friendship, specifically related to Artificial Intelligence (AI) chatbot utility trends, asking: In what ways can AI serve as our critical friend? And, what are the affordances and limitations of AI as a critical friend, compared to our human critical friends? To answer these questions, the authors selected Pi.ai/talk, an AI tool they later nicknamed Pia. After interacting with Pia (using data from a previous study), they compared Pia’s responses to those of two human critical friends, Anne and Anders, who originally provided feedback in their previous self-study. Drawing on theoretical and conceptual perspectives (Thought Collective, Anatomy of Critical Friend Attributes, & Critical Friend Definition Continuum) and using four data sources, the authors applied multiple qualitative analysis tools to determine the affordances and limitations of Pia. They found Pia could serve as a critical friend when examining her critical friend attributes because to some extent she provided feedback to their thinking, probed thoughtful questions, and remained in a positive tone. Although Pia had some affordances in her ability to serve as a critical friend, the data evidenced several limitations in how she met the critical attributes in the areas of constraints, trust, and relational dynamics; especially when juxtaposing Pia’s feedback to Anne’s and Anders’ feedback. Given the findings, the authors argue AI tools can complement human critical friends in self-study research, but as of the date of this publication, they do not advocate for replacing human critical friends entirely.

AI in medical research

This review delves into the possible role of artificial intelligence (AI) in medical research, from planning to publication. AI can aid in idea generation, data analysis, and writing, with tools like chatbots and transcription systems enhancing efficiency. However, AI's limitations, including the "hallucination" problem in which it generates false information, require careful use and verification. Ensuring anonymity compliance with sensitive data is also vital. AI's transformative potential in research brings opportunities for innovation, necessitating mindful application to manage biases and data accuracy.

The role of AI in theatre: Exploring the creation of AI-generated stage plays

This paper presents an in-depth exploration of the integration of Artificial Intelligence (AI) in theatre, with a focus on the creation of AI-generated stage plays. Utilizing ChatGPT's language model and deep learning algorithms, the research investigates AI's capability to generate compelling scripts, characters, and narratives for theatrical productions. The study employs a mixed-methods approach, analyzing existing AI-generated theatrical works and conducting experimental script creation using AI. The findings highlight both the potential and limitations of AI in theatre, emphasizing the need to balance AI-generated content with the intrinsic human qualities of theatrical performances. The paper contributes to the understanding of AI's impact on theatre and raises questions about audience engagement and authenticity in AI-generated content.

Real Estate Insights AI: real estate’s new roommate – the good, the bad and the algorithmic

PurposeThis paper aims to provide a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and Large Language Models (LLMs), such as GPT-4, on the real estate industry. It explores how these technologies are reshaping various aspects of the sector, from market analysis and valuation to customer interactions and evaluates the balance between technological efficiency and the preservation of human elements in business.Design/methodology/approachThe study is based on an analysis of the strengths and weaknesses of AI as a technology in applications for real estate. It uses this framework to assess the potential of this technology in different use cases. This is supplemented by an emerging literature on the topic, practical insights and industry expert opinions to provide a balanced perspective on the subject.FindingsThe paper reveals that AI and LLMs offer significant benefits in real estate, including enhanced data-driven decision-making, predictive analytics and operational efficiency. However, it also uncovers critical challenges, such as potential biases in AI algorithms and the risk of depersonalising customer interactions.Practical implicationsThe paper advocates for a balanced approach to adopting AI, emphasising the importance of understanding its strengths and limitations while ensuring ethical usage in the diverse and complex landscape of real estate.Originality/valueThis work stands out for its balanced examination of both the advantages and limitations of AI in real estate. It introduces the novel concept of the “jagged technological frontier” in real estate, providing a unique framework for understanding the interplay between AI and human expertise in the industry.

Rogue AI: Cautionary Cases in Neuroradiology and What We Can Learn From Them.

Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow. This paper explores AI's impact on neuroradiology, an area accounting for a substantial portion of recent radiology studies. We present a case series evaluating an AI software's performance in detecting neurovascular findings, highlighting five cases where AI interpretations differed from radiologists' assessments. Our study underscores common pitfalls of AI in the context of CT head angiograms, aiming to guide future AI algorithms. Methods We conducted a retrospective case series study at Stony Brook University Hospital, a large medical center in Stony Brook, New York, spanning from October 1, 2021 to December 31, 2021, analyzing 140 randomly sampled CT angiograms using AI software. This software assessed various neurovascular parameters, and AI findings were compared with neuroradiologists' interpretations. Five cases with divergent interpretations were selected for detailed analysis. Results Five representative cases in which AI findings were discordant with radiologists' interpretations are presented with diagnoses including diffuse anoxic ischemic injury, cortical laminar necrosis, colloid cyst, right superficial temporal artery-to-middle cerebral artery(STA-MCA) bypass, and subacute bilateral subdural hematomas. Discussion The errors identified in our case series expose AI's limitations in radiology. Our case series reveals that AI's incorrect interpretations can stem from complexities in pathology, challenges in distinguishing densities, inability to identify artifacts, identifying post-surgical changes in normal anatomy, sensitivity limitations, and insufficient pattern recognition. AI's potential for improvement lies in refining its algorithms to effectively recognize and differentiate pathologies. Incorporating more diverse training datasets, multimodal data, deep-reinforcement learning, clinical context, and real-time learning capabilities are some ways to improve AI's performance in the field of radiology. Conclusion Overall, it is apparent that AI applications in radiology have much room for improvement before becoming more widely integrated into clinical workflows. While AI demonstrates remarkable potential to aid in diagnosis and streamline workflows, our case series highlights common pitfalls that underscore the need for continuous improvement. By refining algorithms, incorporating diverse datasets, embracing multimodal information, and leveraging innovative machine learning strategies, AI's diagnostic accuracy can be significantly improved.

Integrating artificial intelligence into the modernization of traditional Chinese medicine industry: a review.

Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.

Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study

ABSTRACT Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine’s first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students’ confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution’s preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students’ confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

The Evolution of Language Learning: Exploring AI's Impact on Teaching English as a Second Language

      The integration of Artificial Intelligence (AI) into language education has revolutionized the teaching of English as a Second Language (ESL). This study explores the impact of AI on ESL instruction and investigates how AI-driven technologies enhance language learning outcomes. By analyzing current pedagogical approaches and technological innovations, this research examines how AI augments traditional language teaching methods, providing personalized and adaptive learning experiences for ESL learners.        AI-based language learning platforms offer interactive modules tailored to individual student needs, creating an immersive and efficient learning environment. Additionally, the use of natural language processing (NLP) and machine learning algorithms enables real-time feedback and assessment, empowering learners to improve their language skills independently. This study critically evaluates the effectiveness of AI-driven tools in addressing linguistic challenges, such as pronunciation, grammar, and comprehension, while also considering their socio-cultural implications in diverse learning contexts.          By examining the evolving role of AI in ESL education, this research contributes to a comprehensive understanding of the relationship between technology and language pedagogy. The insights from this analysis will inform educators, policymakers, and stakeholders about the potential and limitations of AI in reshaping language acquisition. Ultimately, this exploration advocates for a synergistic approach that harnesses AI's capabilities to optimize ESL instruction while preserving the human elements essential for holistic language learning experiences.

AI and modern experimental biology: A historical perspective

AI and modern experimental biology: A historical perspective Ute Deichmann, Director of the Jacques Loeb Centre for the History and Philosophy of the Life Sciences at Ben-Gurion University of the Negev, discusses the adoption and limitations of Artificial Intelligence within modern experimental biology. ‘How generative AI could disrupt scientific publishing.’ ‘ChatGPT use shows that the grant-application system is broken.’ ‘Companies say the technology will lead to faster drug development. Independent verification and clinical trials will determine whether the claim holds up.’ ‘AlphaFold touted as next big thing for drug discovery — but is it?’ These recent headlines from the journal Nature not only indicate the enormous impact of artificial intelligence (AI), the intelligence of machines or software, on research across various scientific disciplines but also point to challenges regarding AI reliability. What does intelligence here mean? The Cambridge Dictionary defines intelligence as ‘the ability to learn, understand, and make judgments or have opinions based on reason.’

On the Limits of Artificial Intelligence (AI) in Education

The recent hyperbole around artificial intelligence (AI) has impacted on our ability to properly consider the lasting educational implications of this technology. This paper outlines a number critical issues and concerns that need to feature more prominently in future educational discussions around AI. These include: (i) the limited ways in which educational processes and practices can be statistically modelled and calculated; (ii) the ways in which AI technologies risk perpetuating social harms for minoritized students; (iii) the losses incurred through reorganising education to be more ‘machine readable’; and (iv) the ecological and environmental costs of data-intensive and device-intensive forms of AI. The paper concludes with a call for slowing down and recalibrating current discussions around AI and education – paying more attention to issues of power, resistance and the possibility of re-imagining education AI along more equitable and educationally beneficial lines.

Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children?

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike.

Artificial intelligence in the detection of skin cancer: State of the art

The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are effective tools supporting diagnosticians in many medical specialties. In this contribution, a number of applications of different classes of AI algorithms for the detection of this skin melanoma are presented and evaluated. Both classic systems based on the analysis of dermatoscopic images as well as total body systems, enabling the analysis of the patient's whole body to detect moles and pathologic changes, are discussed. These increasingly popular applications that allow the analysis of lesion images using smartphones are also described. The quantitative evaluation of the discussed systems with particular emphasis on the method of validation of the implemented algorithms is presented. The advantages and limitations of AI in the analysis of lesion images are also discussed, and problems requiring a solution for more effective use of AI in dermatology are identified.

Applications of artificial intelligence in anesthesia: A systematic review.

This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.

ChatGPT ile işbirliği yapmak: Alman Dili Eğitimi Üzerine Keşfedici Bir Çalışma

The continuous advancements in artificial intelligence technology bring about changes in foreign language learning. One of the artificial intelligence applications, ChatGPT, developed by OpenAI, draws attention for its applicability in foreign language education. This study explores the opportunities provided by ChatGPT for the learning of four fundamental language skills (listening, reading, speaking, writing) in the context of German language education. The advantages and disadvantages of ChatGPT in foreign language education are examined. Recommendations are provided regarding the points to be considered when utilizing artificial intelligence for the acquisition of the four language skills. Additionally, the disadvantages and limitations of artificial intelligence are discussed.&#x0D; In conclusion, artificial intelligence offers new opportunities in foreign language education. Artificial intelligence applications like ChatGPT can assist students in developing communication skills in a foreign language. Particularly, ChatGPT is believed to have a significant role in enhancing reading and writing skills. However, the disadvantages and limitations of artificial intelligence should not be overlooked. Careful utilization of artificial intelligence in foreign language learning can play a crucial role in students' skill development and improvement of their communication abilities.

Artificial Intelligence in Cardiology Research: How to Upskill

As the use of artificial intelligence (AI) in medicine grows, it is imperative for physicians to understand how it can help shape their practice. Whether one wishes to develop novel applications of AI in medicine or become an early-adopter and end-user of AI-based platforms, an in-depth understanding of the capabilities and limitations of AI is crucial. In this brief article, we aim to provide the reader an introduction into machine learning methods, a review of current efforts to integrate AI into cardiology research and clinical implementation, and practical steps for cardiologists and cardiology trainees to begin utilizing these remarkable tools.

Assessing the Efficacy of AI Segmentation in Diagnostics of Nine Supernumerary Teeth in a Pediatric Patient

We present a very rare case of a child with nine supernumerary teeth to analyze the potential, benefits, and limitations of artificial intelligence, as well as two commercial tools for tooth segmentation. Artificial intelligence (AI) is increasingly finding applications in dentistry today, particularly in radiography. Special attention is given to models based on convolutional neural networks (CNN) and their application in automatic segmentation of the oral cavity and tooth structures. The integration of AI is gaining increasing attention, and the automation of the detection and localization of supernumerary teeth can accelerate the treatment planning process. Despite advancements in 3D segmentation techniques, relying on trained professionals remains crucial. Therefore, human expertise should remain key, and AI should be seen as a support rather than a replacement. Generally, a comprehensive tool that can satisfy all clinical needs in terms of supernumerary teeth and their segmentation is not yet available, so it is necessary to incorporate multiple tools into practice.

The performance of ChatGPT on orthopaedic in-service training exams: A comparative study of the GPT-3.5 turbo and GPT-4 models in orthopaedic education

IntroductionThe rapid advancement of artificial intelligence (AI), particularly the development of Large Language Models (LLMs) such as Generative Pretrained Transformers (GPTs), has revolutionized numerous fields. The purpose of this study is to investigate the application of LLMs within the realm of orthopaedic in training examinations. MethodsQuestions from the 2020–2022 Orthopaedic In-Service Training Exams (OITEs) were given to OpenAI's GPT-3.5 Turbo and GPT-4 LLMs, using a zero-shot inference approach. Each model was given a multiple-choice question, without prior exposure to similar queries, and their generated responses were compared to the correct answer within each OITE. The models were evaluated on overall accuracy, performance on questions with and without media, and performance on first- and higher-order questions. ResultsThe GPT-4 model outperformed the GPT-3.5 Turbo model across all years and question categories (2022: 67.63% vs. 50.24%; 2021: 58.69% vs. 47.42%; 2020: 59.53% vs. 46.51%). Both models showcased better performance with questions devoid of associated media, with GPT-4 attaining accuracies of 68.80%, 65.14%, and 68.22% for 2022, 2021, and 2020, respectively. GPT-4 outscored GPT-3.5 Turbo on first-order questions across all years (2022: 63.83% vs. 38.30%; 2021: 57.45% vs. 50.00%; 2020: 65.74% vs. 53.70%). GPT-4 also outscored GPT-3.5 Turbo on higher-order questions across all years (2022: 68.75% vs. 53.75%; 2021: 59.66% vs. 45.38%; 2020: 53.27% vs. 39.25%). DiscussionGPT-4 showed improved performance compared to GPT-3.5 Turbo in all tested categories. The results reflect the potential and limitations of AI in orthopaedics. GPT-4's performance is comparable to a second-to-third-year resident and GPT-3.5 Turbo's performance is comparable to a first-year resident, suggesting the application of current LLMs can neither pass the OITE nor substitute orthopaedic training. This study sets a precedent for future endeavors integrating GPT models into orthopaedic education and underlines the necessity for specialized training of these models for specific medical domains.

"You Are Not Alone": The Allure and Limitations of Artificial Intelligence in Serious Illness Communication.

Artificial intelligence (AI) is changing the way clinicians practice medicine, and recent technological advancements have resulted in consumer-facing products that can respond to users with dynamic and nuanced language. Clinicians typically struggle with serious illness communication, such as delivering news about a poor prognosis. Palliative care clinicians receive extensive training in serious illness communication, but there is a paucity of such highly trained specialists. This article explores the allure of employing AI-powered chatbots to assist nonspecialist clinicians with serious illness communication and highlights the ethical and practical drawbacks. While outsourcing communication to new AI chatbot technologies may be inappropriate, there is a role for AI in training clinicians on effective language to use when discussing serious illness with their patients.