Artificial Intelligence and Clinical Decision Making: The New Nature of Medical Uncertainty

Innovating dental diagnostics: ChatGPT's accuracy on diagnostic challenges

INTRODUCTION

Complex patient diagnoses in dentistry require a multifaceted approach which combines interpretations of clinical observations with an in-depth understanding of patient history and presenting problems. The present study aims to elucidate the implications of ChatGPT (OpenAI) as a comprehensive diagnostic tool in the dental clinic through examining the chatbot's diagnostic performance on challenging patient cases retrieved from the literature.

METHODS

Our study subjected ChatGPT3.5 and ChatGPT4 to descriptions of patient cases for diagnostic challenges retrieved from the literature. Sample means were compared using a two-tailed t-test, while sample proportions were compared using a two-tailed χ2 test. A p-value below the threshold of 0.05 was deemed statistically significant.

RESULTS

When prompted to generate their own differential diagnoses, ChatGPT3.5 and ChatGPT4 achieved a diagnostic accuracy of 40% and 62%, respectively. When basing their diagnostic processes on a differential diagnosis retrieved from the literature, ChatGPT3.5 and ChatGPT4 achieved a diagnostic accuracy of 70% and 80%, respectively.

CONCLUSION

ChatGPT displays an impressive capacity to correctly diagnose complex diagnostic challenges in the field of dentistry. Our study paints a promising potential for the chatbot to 1 day serve as a comprehensive diagnostic tool in the dental clinic.

Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques

In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.

Sociotechnical imaginaries in academic medicine strategic planning: a document analysis

Purpose Along with other industries, healthcare is becoming increasingly digitized. Our study explores how the field of academic medicine is preparing for this digital future. Method Active strategic plans available in English were collected from faculties of medicine in Canada (n = 14), departments in medical schools (n = 17), academic health science centres (n = 23) and associated research institutes (n = 5). In total, 59 strategic plans were subjected to a practice-oriented form of document analysis, informed by the concept of sociotechnical imaginaries. Results On the one hand, digital health is discursively treated as a continuation of the academic medicine vision, with expansions of physician competencies and of research institutes contributions. These imaginaries do not necessarily disrupt the field of academic medicine as currently configured. On the other hand, there is a vision of digital health pursuing a robust sociotechnical future with transformative implications for how care is conducted, what forms of knowledge are prioritized, how patients and patienthood will be understood, and how data work will be distributed. This imaginary may destabilize existing distributions of knowledge and power. Conclusions Looking through the lens of sociotechnical imaginaries, this study illuminates strategic plans as framing desirable futures, directing attention towards specific ways of understanding problems of healthcare, and mobilizing the resources to knit together social and technical systems in ways that bring these visions to fruition. There are bound to be tensions as these sociotechnical imaginaries are translated into material realities. Many of those tensions and their attempted resolutions will have direct implications for the expectations of health professional graduates, the nature of clinical learning environments, and future relationships with patients. Sociology of digital health and science and technology studies can provide useful insights to guide leaders in academic medicine shaping these digital futures.

Artificial Intelligent Agent Architecture and Clinical Decision-Making in the Healthcare Sector

This paper examines the decision-making processes of physicians and intelligent agents within the healthcare sector, particularly focusing on their characteristics, architectures, and approaches. We provide a theoretical insight into the evolving role of artificial intelligence (AI) in healthcare, emphasizing its potential to address various healthcare challenges. Defining features of intelligent agents are explored, including their perceptual abilities and behavioral properties, alongside their architectural frameworks, ranging from reflex-based to general learning agents, and contrasted with the rational decision-making structure employed by physicians. Through data collection, hypothesis generation, testing, and reflection, physicians exhibit a nuanced approach informed by adaptability and contextual understanding. A comparative analysis between intelligent agents and physicians reveals both similarities and disparities, particularly in adaptability and contextual comprehension. While intelligent agents offer promise in enhancing clinical decisions, challenges with types of dataset biases pose significant hurdles. Informing and educating physicians about AI concepts can build trust and transparency in intelligent programs. Such efforts aim to leverage the strengths of both human and AI toward improving healthcare delivery and outcomes.

Confounding factors affecting the clinical decision-making of nursing and midwifery students post-pandemic COVID-19: cross-sectional study in Jordan

BACKGROUND

The ability of a nurse to make effective clinical decisions is the most important factor that can affect the treatment quality. However, several factors can affect the ability of nursing and midwifery students to make effective clinical decisions.

OBJECTIVES

This study aims to identify the confounding factors that may affect the clinical decision making of nurses and thus patient outcomes after the COVID-19 pandemic in Jordan.

METHODS

A descriptive cross-sectional design was employed in this study. An online self-administered questionnaire was distributed to 269 nursing and midwifery students selected through purposive sampling, 224 of whom completed the questionnaire. The valid and reliable nursing decision-making instrument, which consisted of 24 items, was employed to gather the data, and descriptive statistics and simple linear regression were employed for the data analysis. Data was collected from November to the end of December 2022.

RESULTS

Among the respondents, 72.8% were female, and the average age was 20.79 years (SD = 1.44). The vast majority of the respondents (94.6%) was unmarried, and 74.1% were pursuing a nursing degree. The simple linear regression analysis showed that clinical decision making had a negative and significant relationship with social media usage of an average of 6 h a day (β=-0.085). Moreover, the male nursing students obtained lower clinical decision-making scores (β= -0.408) compared with the female nursing students.

CONCLUSION

Social media usage and gender have a considerable effect on the clinical decision making of the nursing and midwifery students. Therefore, the confounding factors that can affect the clinical decision making of nurses should be discussed further, and strategies to address such factors should be implemented.

Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers

PURPOSE

Many countries have developed clinical decision-making support tools, such as the smart work injury management (SWIM) system in Hong Kong, to predict rehabilitation paths and address global issues related to work injury disability. This study aims to evaluate the accuracy of SWIM by comparing its predictions on real work injury cases to those made by human case managers, specifically with regard to the duration of sick leave and the percentage of permanent disability.

METHODS

The study analyzed a total of 442 work injury cases covering the period from 2012 to 2020, dividing them into non-litigated and litigated cases. The Kruskal-Wallis post hoc test with Bonferroni adjustment was used to evaluate the differences between the actual data, the SWIM predictions, and the estimations made by three case managers. The intra-class correlation coefficient was used to assess the inter-rater reliability of the case managers.

RESULTS

The study discovered that the predictions made by the SWIM model and a case manager possessing approximately 4 years of experience in case management exhibited moderate reliability in non-litigated cases. Nevertheless, there was no resemblance between SWIM's predictions regarding the percentage of permanent disability and those made by case managers.

CONCLUSION

The findings indicate that SWIM is capable of replicating the sick leave estimations made by a case manager with an estimated 4 years of case management experience, albeit with limitations in generalizability owing to the small sample size of case managers involved in the study.

IMPLICATIONS

These findings represent a significant advancement in enhancing the accuracy of CDMS for work injury cases in Hong Kong, signaling progress in the field.

Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI-based decision-making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision-making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision-making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision-making tools are data security, data representation, and explainability of AI-based outcome predictions, in particular for decision-making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential.

Teaching clinical reasoning: principles from the literature to help improve instruction from the classroom to the bedside

Clinical reasoning has been characterized as being an essential aspect of being a physician. Despite this, clinical reasoning has a variety of definitions and medical error, which is often attributed to clinical reasoning, has been reported to be a leading cause of death in the United States and abroad. Further, instructors struggle with teaching this essential ability which often does not play a significant role in the curriculum. In this article, we begin with defining clinical reasoning and then discuss four principles from the literature as well as a variety of techniques for teaching these principles to help ground an instructors' understanding in clinical reasoning. We also tackle contemporary challenges in teaching clinical reasoning such as the integration of artificial intelligence and strategies to help with transitions in instruction (e.g., from the classroom to the clinic or from medical school to residency/registrar training) and suggest next steps for research and innovation in clinical reasoning.

Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management

BACKGROUND

The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications.

METHODS

A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas.

RESULTS

A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively.

CONCLUSION

DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

The Role of Artificial Intelligence in Surgery: What do General Surgery Residents Think?

BACKGROUND

Artificial intelligence (AI) holds significant potential in medical education and patient care, but its rapid emergence presents ethical and practical challenges. This study explored the perspectives of surgical residents on AI's role in medicine.

METHODS

We performed a cross-sectional study surveying general surgery residents at a university-affiliated teaching hospital about their views on AI in medicine and surgical training. The survey covered demographics, residents' understanding of AI, its integration into medical practice, and use of AI tools like ChatGPT. The survey design was inspired by a recent national survey and underwent pretesting before deployment.

RESULTS

Of the 31 participants surveyed, 24% identified diagnostics as AI's top application, 12% favored its use in identifying anatomical structures in surgeries, and 20% endorsed AI integration into EMRs for predictive models. Attitudes toward AI varied based on its intended application: 77.41% expressed concern about AI making life decisions and 70.97% felt excited about its application for repetitive tasks. A significant 67.74% believed AI could enhance the understanding of medical knowledge. Perception of AI integration varied with AI familiarity (P = .01), with more knowledgeable respondents expressing more positivity. Moreover, familiarity influenced the perceived academic use of ChatGPT (P = .039) and attitudes toward AI in operating rooms (P = .032). Conclusion: This study provides insights into surgery residents' perceptions of AI in medical practice and training. These findings can inform future research, shape policy decisions, and guide AI development, promoting a harmonious collaboration between AI and surgeons to improve both training and patient care.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

Clinical use of artificial intelligence requires AI-capable organizations

Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review

Background

Significant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated into real-time patient care.

Objective

The aim of this systematic review is to understand the trends and gaps in research on XAI through an assessment of the essential properties of XAI and an evaluation of explanation effectiveness in the healthcare field.

Methods

A search of PubMed and Embase databases for relevant peer-reviewed articles on development of an XAI model using clinical data and evaluating explanation effectiveness published between January 1, 2011, and April 30, 2022, was conducted. All retrieved papers were screened independently by the two authors. Relevant papers were also reviewed for identification of the essential properties of XAI (e.g., stakeholders and objectives of XAI, quality of personalized explanations) and the measures of explanation effectiveness (e.g., mental model, user satisfaction, trust assessment, task performance, and correctability).

Results

Six out of 882 articles met the criteria for eligibility. Artificial Intelligence (AI) users were the most frequently described stakeholders. XAI served various purposes, including evaluation, justification, improvement, and learning from AI. Evaluation of the quality of personalized explanations was based on fidelity, explanatory power, interpretability, and plausibility. User satisfaction was the most frequently used measure of explanation effectiveness, followed by trust assessment, correctability, and task performance. The methods of assessing these measures also varied.

Conclusion

XAI research should address the lack of a comprehensive and agreed-upon framework for explaining XAI and standardized approaches for evaluating the effectiveness of the explanation that XAI provides to diverse AI stakeholders.

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals

PURPOSE

The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals.

METHOD

In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team.

RESULTS

Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care.

CONCLUSIONS

The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.