scholarly journals Key considerations for the use of artificial intelligence in healthcare and clinical research

2021 ◽  
pp. fhj.2021-0128
Author(s):  
Christopher A Lovejoy ◽  
Anmol Arora ◽  
Varun Buch ◽  
Ittai Dayan
Keyword(s):  
Artificial Intelligence ◽  
Clinical Research

scholarly journals Artificial intelligence in clinical research

2016 ◽  
Vol 3 (4) ◽  
pp. 187 ◽  
Author(s):  
Veerabhadra Sanekal Nayak ◽  
Mohammed Saleem Khan ◽  
Bharat Kumar Shukla ◽  
Pranjal R. Chaturvedi
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Survival Rate ◽  
Drug Development ◽  
Clinical Research ◽  
Business Sector ◽  
Planning Phase ◽  
Pancreatic Malignancy ◽  
Present Technique ◽  
Critical Interest

<p>Envision dedicating fifteen years to a critical interest and emptying staggering amount of funds into it, at the same time confronting a disappointment rate of 95 percent. That is the crippling reality for pharmaceutical organizations, which toss billions of dollars consistently toward medications that possible won't work – and after that do a reversal to the planning phase and do it once more. Today's medications go to the business sector after an extensive, very costly process of drug development. It takes anywhere in the range of 10 to 15 years, here and there significantly more, to convey a medication from introductory revelation to the hands of patients – and that voyage can cost billions up to 12 billion, to be correct. That is just a lot to spend, and excessively yearn for patients to hold up. Patients can hardly wait 15 years for a lifesaving drug, we require another productive focused on medication revelation and improvement process. Artificial Intelligence, can significantly reduce the time included, and also cut the expenses by more than half. This is made conceivable through a totally distinctive way to deal with medication revelation. With the present technique, for each 100 medications that achieve first stage clinical trials, only one goes ahead to wind up a genuine treatment. That is stand out percent, it's an unsustainable model, particularly when there are ailments, for example, pancreatic malignancy which has a normal five-year survival rate of 6%.</p>

scholarly journals Review of Clinical Research Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 193-202
Author(s):  
Anthony Solomonides
Keyword(s):  
Artificial Intelligence ◽  
Clinical Research ◽  
Big Data Analytics ◽  
Medical Knowledge ◽  
Data Models ◽  
Research Areas ◽  
Personal Awareness ◽  
Real World Evidence ◽  
Clinical Research Informatics ◽  
Active Research

Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

scholarly journals Clinical Research in Pneumonia: Role of Artificial Intelligence

2019 ◽  
Vol 3 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Timothy Wiemken ◽  
Robert Kelley ◽  
William Mattingly ◽  
Julio Ramirez
Keyword(s):  
Artificial Intelligence ◽  
Clinical Research

Columns

2019 ◽  
Vol 23 (11) ◽  
pp. 12-23
Keyword(s):  
Artificial Intelligence ◽  
Antimicrobial Resistance ◽  
Clinical Research ◽  
Preterm Births ◽  
Diagnosis And Treatment ◽  
The Way ◽  
Research Studies

The following topics are under this section: Preterm Births? Diet not the only answer The key in diagnosis and treatment Tackling Antimicrobial Resistance Clinical research studies pave the way for better healthcare Up in the Clouds with Artificial Intelligence and Healthcare

scholarly journals Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease

2019 ◽  
Vol 8 (17) ◽  
Author(s):  
Nobuyuki Kagiyama ◽  
Sirish Shrestha ◽  
Peter D. Farjo ◽  
Partho P. Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Disease ◽  
Clinical Research

scholarly journals “Artificial intelligence”: Which services, which applications, which results and which development today in clinical research? Which impact on the quality of care? Which recommendations?

Therapies ◽  
2019 ◽  
Vol 74 (1) ◽  
pp. 155-164 ◽  
Author(s):  
Vincent Diebolt ◽  
Isaac Azancot ◽  
François-Henri Boissel ◽  
Isabelle Adenot ◽  
Christine Balague ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Quality Of Care ◽  
Clinical Research

Clinical research underlies ethical integration of healthcare artificial intelligence

2020 ◽  
Vol 26 (9) ◽  
pp. 1325-1326
Author(s):  
Melissa D. McCradden ◽  
Elizabeth A. Stephenson ◽  
James A. Anderson
Keyword(s):  
Artificial Intelligence ◽  
Clinical Research

scholarly journals Charting a Path Forward for Clinical Research in Artificial Intelligence and Gastroenterology

2021 ◽  
Author(s):  
Jeremy R. Glissen Brown ◽  
Akbar K. Waljee ◽  
Yuichi Mori ◽  
Prateek Sharma ◽  
Tyler M. Berzin
Keyword(s):  
Artificial Intelligence ◽  
Clinical Research

scholarly journals Artificial Intelligence in Medicine: Chances and Challenges for Wide Clinical Adoption

2020 ◽  
Vol 36 (6) ◽  
pp. 443-449
Author(s):  
Julian Varghese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Research ◽  
Structured Data ◽  
Regulatory Aspects ◽  
Clinical Benefits ◽  
The Subject ◽  
Common Barriers ◽  
Clinical Adoption

<b><i>Background:</i></b> Artificial intelligence (AI) applications that utilize machine learning are on the rise in clinical research and provide highly promising applications in specific use cases. However, wide clinical adoption remains far off. This review reflects on common barriers and current solution approaches. <b><i>Summary:</i></b> Key challenges are abbreviated as the RISE criteria: Regulatory aspects, Interpretability, interoperability, and the need for Structured data and Evidence. As reoccurring barriers of AI adoption, these concepts are delineated and complemented by points to consider and possible solutions for effective and safe use of AI applications. <b><i>Key Messages:</i></b> There is a fraction of AI applications with proven clinical benefits and regulatory approval. Many new promising systems are the subject of current research but share common issues for wide clinical adoption. The RISE criteria can support preparation for challenges and pitfalls when designing or introducing AI applications into clinical practice.

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