Intelligence In Medicine Research Articles

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine.

Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

Artificial intelligence in medicine: has the time come to hang up the stethoscope?

The question of whether the time has come to hang up the stethoscope is bound up in the promises of artificial intelligence (AI), promises that have so far proven difficult to deliver, perhaps because of the mismatch between the technical capability of AI and its use in real-world clinical settings. This perspective argues that it is time to move away from discussing the generalised promise of disembodied AI and focus on specifics. We need to focus on how the computational method underlying AI, i.e. machine learning (ML), is embedded into tools, how those tools contribute to clinical tasks and decisions and to what extent they can be relied on. Accordingly, we pose four questions that must be asked to make the discussion real and to understand how ML tools contribute to health care: (1) What does the ML algorithm do? (2) How is output of the ML algorithm used in clinical tools? (3) What does the ML tool contribute to clinical tasks or decisions? (4) Can clinicians act or rely on the ML tool? Two exemplar ML tools are examined to show how these questions can be used to better understand the role of ML in supporting clinical tasks and decisions. Ultimately, ML is just a fancy method of automation. We show that it is useful in automating specific and narrowly defined clinical tasks but likely incapable of automating the full gamut of decisions and tasks performed by clinicians.

Defending explicability as a principle for the ethics of artificial intelligence in medicine

The difficulty of explaining the outputs of artificial intelligence (AI) models and what has led to them is a notorious ethical problem wherever these technologies are applied, including in the medical domain, and one that has no obvious solution. This paper examines the proposal, made by Luciano Floridi and colleagues, to include a new ‘principle of explicability’ alongside the traditional four principles of bioethics that make up the theory of ‘principlism’. It specifically responds to a recent set of criticisms that challenge the supposed need for such a principle to perform an enabling role in relation to the traditional four principles and therefore suggest that these four are sufficient without the addition of explicability. The paper challenges the critics’ premise that explicability cannot be an ethical principle like the classic four because it is explicitly subordinate to them. It argues instead that principlism in its original formulation locates the justification for ethical principles in a midlevel position such that they mediate between the most general moral norms and the contextual requirements of medicine. This conception of an ethical principle then provides a mold for an approach to explicability on which it functions as an enabling principle that unifies technical/epistemic demands on AI and the requirements of high-level ethical theories. The paper finishes by anticipating an objection that decision-making by clinicians and AI fall equally, but implausibly, under the principle of explicability’s scope, which it rejects on the grounds that human decisions, unlike AI’s, can be explained by their social environments.

Artificial Intelligence in Medicine: Potential Benefits and Risks

Abstract not available
 TAJ 2022; 36: No-1: I-II

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

Artificial intelligence in medicine

Artificial intelligence in medicine

Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review

The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms between each model, but have reached controversial conclusions. Thus, the performance of current machine-learning-based gastric cancer risk prediction models alongside the clinical relevance of different predictive factors needs to be evaluated to help build more efficient and feasible models in the future. In this systematic review, we summarize the current research progress related to the gastric cancer risk prediction model; discuss the predictive factors and methods used to construct the model; analyze the role of important predictive factors in gastric cancer, the preference of the selected classification algorithm, and the emphasis of evaluation criteria; and provide suggestions for the subsequent construction and improvement of the gastric cancer risk prediction model. Finally, we propose an improved approach based on the ethical issues of artificial intelligence in medicine to realize the clinical application of the gastric cancer risk prediction model in the future.

A review on detecting brain tumors using deep learning and magnetic resonance images

Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.

Bridging artificial intelligence in medicine with generative pre-trained transformer (GPT) technology

Bridging artificial intelligence in medicine with generative pre-trained transformer (GPT) technology

An ethical pathway for the use of Artificial Intelligence in Medicine and Nursing Care

Modern Medicine faces the challenge of acquiring, analyzing, and applying a great amount of data to solve a complex quantity of clinical problems. In this scenario, Artificial Intelligence technologies serve the developing of programs to help professionals diagnose, predict results, and make therapeutic decisions. The same as any technology applied to man, the use of Artificial Intelligence poses ethical questions. In this article the principal ethical problems related to the use of Artificial Intelligence technologies in Medicine are identified and a roadmap for reflection on the former from the praxis perspective is suggested. Nursing is no stranger to the revolutionary change brought about by the introduction of Artificial Intelligence in health care. The recent conclusions of the international think-tank on Artificial Intelligence and Nursing recognized the relevance of the use of these technologies to increase and extend the capabilities of Nursing, including care. Nursing practice raises the question about the impact of Artificial Intelligence technologies on the very essence of Nursing, that is, on the nurse-patient relationship by providing good care. From Nursing epistemology, it is to possible sketch out a pathway for reflection on the ethical problems raised by the use of Artificial Intelligence technologies.

Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care.

The increasingly sophisticated and rapidly evolving application of artificial intelligence in medicine is transforming how health care is delivered, highlighting a need for current and future physicians to develop basic competency in the data science that underlies this topic. Medical educators must consider how to incorporate central concepts in data science into their core curricula to train physicians of the future. Similar to how the advent of diagnostic imaging required the physician to understand, interpret, and explain the relevant results to patients, physicians of the future should be able to explain to patients the benefits and limitations of management plans guided by artificial intelligence. We outline major content domains and associated learning outcomes in data science applicable to medical student curricula, suggest ways to incorporate these themes into existing curricula, and note potential implementation barriers and solutions to optimize the integration of this content.

Can Artificial Intelligence Ever Develop the Human Touch and Replace a Psychiatrist? - A letter to the editor of the Journal of Medical Systems: Regarding “Artificial Intelligence in Medicine & ChatGPT: De-Tether the Physician”

Can Artificial Intelligence Ever Develop the Human Touch and Replace a Psychiatrist? - A letter to the editor of the Journal of Medical Systems: Regarding “Artificial Intelligence in Medicine & ChatGPT: De-Tether the Physician”

Maximizing the potential of Artificial Intelligence in medicine while maintaining ethical standards and addressing limitations

Maximizing the potential of Artificial Intelligence in medicine while maintaining ethical standards and addressing limitations

Identification of milling status based on vibration signals using artificial intelligence in robot-assisted cervical laminectomy

BackgroundWith advances in science and technology, the application of artificial intelligence in medicine has significantly progressed. The purpose of this study is to explore whether the k-nearest neighbors (KNN) machine learning method can identify three milling states based on vibration signals: cancellous bone (CCB), ventral cortical bone (VCB), and penetration (PT) in robot-assisted cervical laminectomy.MethodsCervical laminectomies were performed on the cervical segments of eight pigs using a robot. First, the bilateral dorsal cortical bone and part of the CCB were milled with a 5 mm blade and then the bilateral laminae were milled to penetration with a 2 mm blade. During the milling process using the 2 mm blade, the vibration signals were collected by the acceleration sensor, and the harmonic components were extracted using fast Fourier transform. The feature vectors were constructed with vibration signal amplitudes of 0.5, 1.0, and 1.5 kHz and the KNN was then trained by the features vector to predict the milling states.ResultsThe amplitudes of the vibration signals between VCB and PT were statistically different at 0.5, 1.0, and 1.5 kHz (P < 0.05), and the amplitudes of the vibration signals between CCB and VCB were significantly different at 0.5 and 1.5 kHz (P < 0.05). The KNN recognition success rates for the CCB, VCB, and PT were 92%, 98%, and 100%, respectively. A total of 6% and 2% of the CCB cases were identified as VCB and PT, respectively; 2% of VCB cases were identified as PT.ConclusionsThe KNN can distinguish different milling states of a high-speed bur in robot-assisted cervical laminectomy based on vibration signals. This method is feasible for improving the safety of posterior cervical decompression surgery.

Artificial intelligence in medicine

Artificial intelligence in medicine

Ethical issues of implementing artificial intelligence in medicine

Artificial intelligence (AI) systems are highly efficient. However, their implementation in medical practice is accompanied by a range of ethical issues. The black box problem is basic to the AI philosophy, although having its own specificity in relation to medicine. A selection of relevant papers for the last three years by citations and their analysis through PubMed and Google Scholar search engines was conducted to study the problems of the AI implementation in medicine. One of the central problems is that the algorithms to justify decisions are still unclear to doctors and patients. The lack of clear and reasonable principles of AI operation is called the black box problem. How can doctors rely on AI findings without enough data to explain a particular decision? Who will be responsible for the final decision in case of an adverse outcome (death or serious injury)? In routine practice, medical decisions are based on an integrative approach (understanding of pathophysiology and biochemistry and interpretation of past findings), clinical trials and cohort studies. AI may be used to build a plan for disease diagnosis and treatment, while not providing a convincing justification for specific decisions. This creates a black box, since the information that the AI considers important for making a conclusion is not always clear, nor is it clear how or why the AI reaches that conclusion. Thus, Juan M. Durn writes, Even if we claim to understand the principles underlying AI annotation and training, it is still difficult and often even impossible to understand the inner workings of such systems. The doctor can interpret or verify the results of these algorithms, but cannot explain how the algorithm arrived at its recommendations or diagnosis. Currently, AI models are trained to recognize microscopic adenomas and polyps in the colon. However, doctors still have insufficient understanding of how AI differentiates between different types of polyps despite the high accuracy, and the signs that are key to making an AI diagnosis remain unclear to experienced endoscopists. Another example is the biomarkers of colorectal cancer recognized by AI. The doctor does not know how algorithms determine the quantitative and qualitative criteria of detectable biomarkers to formulate a final diagnosis in each individual case, i.e., a black box of process pathology emerges. For the trust of doctors and patients to be earned, the processes underlying the work of AI must be deciphered and explained, describing how it is done sequentially, step by step, and a specific result is to be formulated. Although the black box algorithms cannot be called transparent, the possibility of applying these technologies in practical medicine is worth considering. Despite the above problems, the accuracy and efficiency of solutions does not allow to neglect the use of AI. On the contrary, this use is necessary. Emerging problems should serve as a basis for training and educating doctors to work with AI, expanding the scope of application and developing new diagnostic techniques.

The concept of responsible artificial intelligence as the future of artificial intelligence in medicine

Active deployment of artificial intelligence (AI) systems in medicine creates many challenges. Recently, the concept of responsible artificial intelligence (RAI) was widely discussed, which is aimed at solving the inevitable ethical, legal, and social problems. The scientific literature was analyzed and the possibility of applying the RAI concept to overcome the existing AI problems in medicine was considered. Studies of possible AI applications in medicine showed that current algorithms are unable to meet the basic enduring needs of society, particularly, fairness, transparency, and reliability. The RAI concept based on three principles accountability for AI activities and responsibility and transparency of findings (ART) was proposed to address ethical issues. Further evolution, without the development and application of the ART concept, turns dangerous and impossible the use of AI in such areas as medicine and public administration. The requirements for accountability and transparency of conclusions are based on the identified epistemological (erroneous, non-transparent, and incomplete conclusions) and regulatory (data confidentiality and discrimination of certain groups) problems of using AI in digital medicine [2]. Epistemological errors committed by AI are not limited to omissions related to the volume and representativeness of the original databases analyzed. In addition, these include the well-known black box problem, i.e. the inability to look into the process of forming AI outputs when processing input data. Along with epistemological errors, normative problems inevitably arise, including patient confidentiality and discrimination of some social groups due to the refusal of some patients to provide medical data for training algorithms and as part of the analyzed databases, which will lead to inaccurate AI conclusions in cases of certain gender, race, and age. Importantly, the methodology of the AI data analysis depends on the program code set by the programmer, whose epistemological and logical errors are projected onto the AI. Hence the problem of determining responsibility in the case of erroneous conclusions, i.e. its distribution between the program itself, the developer, and the executor. Numerous professional associations design ethical standards for developers and a statutory framework to regulate responsibility between the links described. However, the state must play the greatest role in the development and approval of such legislation. The use of AI in medicine, despite its advantages, is accompanied by many ethical, legal, and social challenges. The development of RAI has the potential both to solve these challenges and to further the active and secure deployment of AI systems in digital medicine and healthcare.

Искусственный интеллект и нейронные сети в урологии.

Introduction. The review is devoted to the analysis of the world experience in the use of artificial intelligence in medicine and urology. Materials and methods. The review was conducted on the basis of data published in the PubMed, Scientific Electronic Library eLibrary.ru databases and on the professional medical associations websites. The article contains 43 scientific publications. Results. In this literature review, the types of neural networks and the features of their application are considered in detail. Special attention is paid to the use of artificial intelligence in medical imaging, particularly in urology. Effectiveness and accuracy of developing the connectionism method application in artificial neural network (ANN) in recent publications are presented. Advantages and disadvantages of artificial intelligence for a predictive model are outlined. We also considered the main mistakes displayed in publications regarding unreliable or poorly created neural networks and studies in the use of ANNs for clinical practice. Conclusion. The use of neural networks as a tool in the daily clinical practice of a specialist is still limited. The main problems are still the unreliability of the created models and lack of easy-to-use algorithms.

Artificial intelligence in medicine and research - the good, the bad, and the ugly.

Artificial intelligence (AI) broadly refers to machines that simulate intelligent human behavior, and research into this field is exponential and worldwide, with global players such as Microsoft battling with Google for supremacy and market share. This paper reviews the "good" aspects of AI in medicine for individuals who embrace the 4P model of medicine (Predictive, Preventive, Personalized, and Participatory) to medical assistants in diagnostics, surgery, and research. The "bad" aspects relate to the potential for errors, culpability, ethics, data loss and data breaches, and so on. The "ugly" aspects are deliberate personal malfeasances and outright scientific misconduct including the ease of plagiarism and fabrication, with particular reference to the novel ChatGPT as well as AI software that can also fabricate graphs and images. The issues pertaining to the potential dangers of creating rogue, super-intelligent AI systems that lead to a technological singularity and the ensuing perceived existential threat to mankind by leading AI researchers are also briefly discussed.

Artificial Intelligence in Medicine: Revolutionizing Healthcare for Improved Patient Outcomes

Artificial Intelligence in Medicine: Revolutionizing Healthcare for Improved Patient Outcomes