scholarly journals Artificial Intelligence-Assisted Surgery: Potential and Challenges

2020 ◽  
Vol 36 (6) ◽  
pp. 450-455
Author(s):  
Sebastian Bodenstedt ◽  
Martin Wagner ◽  
Beat Peter Müller-Stich ◽  
Jürgen Weitz ◽  
Stefanie Speidel
Keyword(s):  
Artificial Intelligence ◽  
Improve Patient Care ◽  
Data Driven ◽  
Full Potential ◽  
Computer Scientists ◽  
Close Cooperation ◽  
Scientists And Engineers ◽  
The Right ◽  
Cognitive Robotic ◽  
Improve Patient

<b><i>Background:</i></b> Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field. <b><i>Summary:</i></b> This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery. <b><i>Key Messages:</i></b> AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.

scholarly journals How will artificial intelligence and bioinformatics change our understanding of IgA in the next decade?

2021 ◽  
Author(s):  
Roman David Bülow ◽  
Daniel Dimitrov ◽  
Peter Boor ◽  
Julio Saez-Rodriguez
Keyword(s):  
Artificial Intelligence ◽  
Multidisciplinary Approach ◽  
Immunoglobulin A ◽  
Improve Patient Care ◽  
Integrated Analysis ◽  
Clinical Expertise ◽  
End Stage ◽  
The Complement System ◽  
Improve Patient ◽  
Generation Sequencing

AbstractIgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

Sports Medicine and Artificial Intelligence: A Primer

2021 ◽  
pp. 036354652110086
Author(s):  
Prem N. Ramkumar ◽  
Bryan C. Luu ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Benedict U. Nwachukwu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Sports Medicine ◽  
Injury Risk ◽  
Industrial Revolution ◽  
Improve Patient Care ◽  
Great Promise ◽  
Patient Reported ◽  
Athlete Injury ◽  
Improve Patient ◽  
Applications Of Ai

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

ARTIFICIAL INTELLIGENCE IN DENTISTRY: A MILESTONE

2021 ◽  
pp. 49-52
Author(s):  
Gaurvi Vikram Kamra ◽  
Ankur Sharma
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Treatment Planning ◽  
Computational Models ◽  
Improve Patient Care ◽  
Relevant Information ◽  
Documented Information ◽  
Improve Patient

The concept of "articial intelligence" (AI) refers to machines that are capable of executing human-like tasks. AI can also be dened as a eld concerned with computational models that can reason and act intelligently. Perspicacious software for data computation has become a necessity as the amount of documented information and patient data has increased dramatically. The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview. AI has been used in a variety of ways, from processing of data and locating relevant information to using neural networks for diagnosis and the introduction of augmented reality and virtual reality in dental education. AI-based apps will improve patient care by relieving the dental workforce of tedious routine duties, improving population health at lower costs, and eventually facilitating individualized, anticipatory, prophylactic, and collaborative dentistry. The convergence of AI and digitization has ushered in a new age in dentistry, with tremendously promising future prospects.The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview.

scholarly journals Artificial intelligence in arthroplasty

Arthroplasty ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Glen Purnomo ◽  
Seng-Jin Yeo ◽  
Ming Han Lincoln Liow
Keyword(s):  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Recent Literature ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Patient Specific ◽  
Allocation Decision ◽  
Medical Specialties ◽  
Future Direction ◽  
Improve Patient

AbstractArtificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

scholarly journals Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study

Gut ◽  
2019 ◽  
Vol 69 (4) ◽  
pp. 681-690 ◽  
Author(s):  
Cynthia Reichling ◽  
Julien Taieb ◽  
Valentin Derangere ◽  
Quentin Klopfenstein ◽  
Karine Le Malicot ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colon Cancer ◽  
Patient Outcomes ◽  
Improve Patient Care ◽  
Stage Iii ◽  
Free Survival ◽  
Relapse Risk ◽  
Stage Iii Colon Cancer ◽  
Healthy Mucosa ◽  
Improve Patient

ObjectiveDiagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.DesignWe have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.ResultsWithin the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated ‘DGMuneS’, outperformed Immunoscore when used in estimating patients’ prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.ConclusionThese findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients’ prognosis.

scholarly journals Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment

2021 ◽  
Vol 28 (1) ◽  
pp. e100323
Author(s):  
Anthony Wilson ◽  
Haroon Saeed ◽  
Catherine Pringle ◽  
Iliada Eleftheriou ◽  
Paul A Bromiley ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Healthcare Professionals ◽  
Improve Patient Care ◽  
Clinical Environment ◽  
Healthcare Data ◽  
Healthcare Settings ◽  
Personal Experiences ◽  
Set Up ◽  
Personal Reflections ◽  
Improve Patient

There is much discussion concerning ‘digital transformation’ in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use.

scholarly journals On evaluation metrics for medical applications of artificial intelligence

2021 ◽  
Author(s):  
Steven Hicks ◽  
Inga Strüke ◽  
Vajira Thambawita ◽  
Malek Hammou ◽  
Pål Halvorsen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Care ◽  
Open Source ◽  
Performance Metrics ◽  
Improve Patient Care ◽  
Evaluation Metrics ◽  
Medical Applications ◽  
Web Based ◽  
Improve Patient

Clinicians and model developers need to understand how proposed machine learning (ML) models could improve patient care. In fact, no single metric captures all the desirable properties of a model and several metrics are typically reported to summarize a model's performance. Unfortunately, these measures are not easily understandable by many clinicians. Moreover, comparison of models across studies in an objective manner is challenging, and no tool exists to compare models using the same performance metrics. This paper looks at previous ML studies done in gastroenterology, provides an explanation of what different metrics mean in the context of the presented studies, and gives a thorough explanation of how different metrics should be interpreted. We also release an open source web-based tool that may be used to aid in calculating the most relevant metrics presented in this paper so that other researchers and clinicians may easily incorporate them into their research.

scholarly journals AI for Everyone? Critical Perspectives

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Data Driven ◽  
Technological Determinism ◽  
New Era ◽  
The Right ◽  
Critical Perspectives

We are entering a new era of technological determinism and solutionism in which governments and business actors are seeking data-driven change, assuming that Artificial Intelligence is now inevitable and ubiquitous. But we have not even started asking the right questions, let alone developed an understanding of the consequences. Urgently needed is debate that asks and answers fundamental questions about power. This book brings together critical interrogations of what constitutes AI, its impact and its inequalities in order to offer an analysis of what it means for AI to deliver benefits for everyone. The book is structured in three parts: Part 1, AI: Humans vs. Machines, presents critical perspectives on human-machine dualism. Part 2, Discourses and Myths About AI, excavates metaphors and policies to ask normative questions about what is ‘desirable’ AI and what conditions make this possible. Part 3, AI Power and Inequalities, discusses how the implementation of AI creates important challenges that urgently need to be addressed. Bringing together scholars from diverse disciplinary backgrounds and regional contexts, this book offers a vital intervention on one of the most hyped concepts of our times.

scholarly journals Artificial Intelligence in Health Informatics: Hype or Reality?

2019 ◽  
Vol 28 (01) ◽  
pp. 003-004 ◽  
Author(s):  
Kate Fultz Hollis ◽  
Lina F. Soualmia ◽  
Brigitte Séroussi
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Expert Knowledge ◽  
Improve Patient Care ◽  
Health Data ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Data Driven ◽  
Editorial Team ◽  
Special Topic

Objectives: To provide an introduction to the 2019 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: This editorial presents an overview and introduction to the 2019 IMIA Yearbook which includes the special topic “Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications". The special topic is discussed, the IMIA President’s statement is introduced, and changes in the Yearbook editorial team are described. Results: Artificial intelligence (AI) in Medicine arose in the 1970’s from new approaches for representing expert knowledge with computers. Since then, AI in medicine has gradually evolved toward essentially data-driven approaches with great results in image analysis. However, data integration, storage, and management still present clear challenges among which the lack of explanability of the results produced by data-driven AI methods. Conclusion: With more health data availability, and the recent developments of efficient and improved machine learning algorithms, there is a renewed interest for AI in medicine.The objective is to help health professionals improve patient care while also reduce costs. However, the other costs of AI, including ethical issues when processing personal health data by algorithms, should be included.

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