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Monday, May 15, 2023

Exploring Artificial Intelligence & Machine Learning in Drug Development

What is Artificial Intelligence and Machine Learning?

Artificial Intelligence (AI) and Machine Learning (ML) are branches of computer science, statistics, and engineering that use algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is considered a subset of AI that allows models to be developed by training algorithms through data analysis without models being explicitly programmed.

What role is AI/ML playing in drug development?

FDA recognizes the increased use of AI/ML throughout the drug development life cycle and across various therapeutic areas. FDA has seen a significant increase in drug and biologic application submissions using AI/ML features with more additional than 100 submissions reported in 2021. These submissions traverse the landscape of drug development — from drug discovery and clinical research to postmarket safety surveillance and advanced pharmaceutical manufacturing.  

What is the FDA's perspective on using AI/ML in drug development?

FDA is devoted to guaranteeing that pharmaceuticals are safe and efficacious while stimulating technical inventions. However, as with any innovation, AI/ML creates opportunities and new and unique challenges. To meet these challenges, FDA has accelerated its efforts to create an agile regulatory ecosystem that can facilitate innovation while safeguarding public health.

As part of this effort, FDA's Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), issued an initial discussion paper to communicate with a range of stakeholders and to explore relevant considerations for the use of AI/ML in the development of drugs and biological products. The agency will continue to solicit feedback as it advances regulatory science in this area.

AI/ML will undoubtedly play a critical role in drug development. As a result, the FDA plans to develop and adopt a flexible risk-based regulatory framework that promotes innovation and protects patient safety.

https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development?

Wednesday, May 10, 2023

EvidenceHunt - AI-Powered Clinical Evidence Search Engine for Healthcare Professionals

Introduction:

Finding relevant clinical evidence quickly and efficiently is crucial for healthcare professionals. EvidenceHunt (https://evidencehunt.com) is an AI-powered search engine designed to help users find clinical evidence rapidly and effectively. In this post, we will explore the key features and benefits of EvidenceHunt, focusing on its ability to streamline clinical evidence searches, customizable weekly e-alerts, and user-friendly interface.

Streamlined Clinical Evidence Search

  • AI-driven search: EvidenceHunt uses artificial intelligence to facilitate quick and accurate clinical evidence searches, simplifying the process for users.
  • Specialties and custom queries: Users can search for the latest clinical evidence using simple search terms, predefined medical specialties, or their custom PubMed query.
  • Time-saving alternative: EvidenceHunt offers a more efficient alternative to traditional methods like PubMed searches, eliminating the need to sift through thousands of articles.

Weekly E-Alerts for Personalized Updates

  • Stay up-to-date: Users can subscribe to weekly e-alerts tailored to their search interests.
  • Relevant content: E-alerts help healthcare professionals stay informed about the latest clinical trials, new evidence in specific disease areas, and recent findings on particular drugs.

User-Friendly Interface by DeepDoc.io

  • Multidisciplinary team: The user interface is designed by deepdoc.io's multidisciplinary team, providing an optimal user experience.
  • Fast answers: The platform is designed to answer any clinical question quickly, allowing users to make informed decisions in their practice.
  • Accessible information: EvidenceHunt makes it easy for users to find the information they need, even without extensive knowledge of medical search techniques.

Conclusion:

EvidenceHunt is an invaluable resource for healthcare professionals seeking a fast, efficient, and user-friendly way to search for clinical evidence. With its AI-driven search capabilities, customizable weekly e-alerts, and intuitive interface, EvidenceHunt empowers users to stay informed and make well-informed decisions in their practice. So if you're a healthcare professional looking to save time and access the latest clinical evidence effortlessly, EvidenceHunt is an excellent tool to consider.


Visit https://evidencehunt.com to start your search for clinical evidence and stay up-to-date with the latest findings in your field.

Tuesday, May 09, 2023

Challenging the Scientific Publishing Industry & its "Perverse and Needless" Business Model

The scientific publishing industry has been criticized for its "perverse and needless" business model.

  • This model involves scientists creating work under government funding and providing it to publishers for free. 
  • The publisher then sells the product back to institutions at a high price, despite most of the editorial burden being done by working scientists on a volunteer basis. 
  • This arrangement has made scientific research often inaccessible to the general public due to the high cost of accessing academic journals. As a result, many have called for a change in the publishing industry to make scientific research more accessible and affordable for everyone.

This system is often called a "triple-pay" system since the state funds research, pays the salaries of those checking quality control, and buys most published products. However, scientists know they are getting an unfair deal in this situation. 

Scientists argue that the current system benefits publishers more than researchers, as publishers charge high fees for access to their journals and publications. However, this can make it difficult for researchers to access the information they need to conduct their work and can also limit the impact of their research. As a result, there is growing interest in alternative models of publishing that prioritize open access and affordability.

Critics contend that the scientific publishing industry could operate more efficiently and lower costs, promoting scientific progress. The current system involves publishers receiving raw materials from customers, typically scientists, and then requiring them to perform quality control themselves before selling the same materials back at inflated prices. This arrangement excludes the scientific publishing industry and is not observed in any other industry. The criticism is that this process could be streamlined and made more cost-effective, benefiting scientific progress.

Despite the profitability of the scientific publishing industry, there are concerns about its potential impact on science. 

For example, focusing on profit could lead to prioritizing publishing research that is more likely to generate revenue than research that is most important for advancing scientific knowledge. This could ultimately be detrimental to the scientific community as a whole. 

Therefore, it is essential to consider the potential consequences of the financial incentives in scientific publishing and ensure that the pursuit of profit does not come at the expense of scientific progress.

A few major players, including Elsevier, have long dominated the academic publishing industry. However, there have been increasing calls for disruption and change within the industry recently. Despite initial skepticism towards these calls, competition has increased, and pressure has mounted on companies to adapt their practices or risk losing relevance altogether.

Elsevier, one of the largest publishers in the scientific journal industry, primarily operates by providing a platform for scientists to share their research results with a narrow audience. Despite the narrow audience, Elsevier can generate significant profit margins from this core operation. The profitability of scientific publishing has raised questions about its potential impact on science, and it is essential to consider the potential consequences of the financial incentives involved.

In response to these concerns, many universities worldwide have established institutional repositories where faculty members can deposit copies of their published works free of cost so they may be accessed more easily online without having paywalls blocking them off entirely. Additionally, several initiatives, such as Plan S, aimed to make all publicly funded research available via Open Access platforms within specific timeframes after publication.

The scientific publishing industry plays a crucial role in the academic world by providing researchers access to peer-reviewed articles and studies for advancing knowledge in various fields. However, it is vital to ensure that the pursuit of profit does not come at the expense of scientific progress. As such, there is growing interest in alternative models of publishing that prioritize open access and affordability.

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