Curated Papers > Highly Cited Authoritative Literature on "AI Drug Discovery"

AI in health and medicine.

Literature Information

DOI	10.1038/s41591-021-01614-0
PMID	35058619
Journal	Nature medicine
Impact Factor	50.0
JCR Quartile	Q1
Publication Year	2022
Times Cited	723
Keywords	Artificial Intelligence, Medical Image Analysis, Human-AI Collaboration, Technical Challenges, Ethical Issues
Literature Type	Journal Article, Research Support, N.I.H., Extramural, Review
ISSN	1078-8956
Pages	31-38
Issue	28(1)
Authors	Pranav Rajpurkar, Emma Chen, Oishi Banerjee, Eric J Topol

TL;DR

This research paper highlights the transformative potential of artificial intelligence (AI) in medicine, emphasizing significant advancements in medical image analysis and the bridging of research with practical deployment. It also outlines emerging research avenues while addressing critical technical and ethical challenges, suggesting that overcoming these obstacles could enhance the accuracy, efficiency, and accessibility of healthcare globally.

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Artificial Intelligence · Medical Image Analysis · Human-AI Collaboration · Technical Challenges · Ethical Issues

Abstract

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human-AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI's potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

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Primary Questions Addressed

1. What are some specific examples of how AI has improved patient outcomes in clinical settings?
2. How do different types of non-image data sources contribute to advancements in medical AI?
3. What ethical frameworks are being proposed to address issues like racial bias in AI applications in healthcare?
4. In what ways can human-AI collaboration enhance the decision-making process for clinicians?
5. What are the potential long-term impacts of AI on healthcare accessibility in underserved populations?

Key Findings

Key Insights

1. Research Background and Purpose:
   The integration of artificial intelligence (AI) into healthcare is rapidly evolving, with the potential to significantly enhance the experiences of both clinicians and patients. This study stems from a two-year initiative dedicated to systematically tracking and sharing advancements in medical AI. The primary objective is to identify and analyze key developments in the field, focusing on how AI can bridge the gap between research and practical applications in medicine.

2. Main Methods and Findings:
   The researchers employed a systematic review approach, documenting weekly advancements in medical AI over two years. They highlighted significant progress in medical image analysis, which has shown promise in reducing the disparity between theoretical research and real-world deployment. The study also explored innovative research directions beyond traditional image data, including leveraging non-image data sources, exploring unconventional problem formulations, and fostering human-AI collaboration. These findings underscore the versatility of AI applications in medicine and indicate that the field is moving towards more comprehensive and integrated AI solutions.

3. Core Conclusions:
   The study concludes that while AI has the potential to revolutionize healthcare by making it more accurate, efficient, and accessible, there remain considerable technical and ethical challenges that need to be addressed. Issues such as data scarcity and racial bias are significant barriers that can hinder the equitable implementation of AI technologies. The successful resolution of these challenges is crucial for realizing the full potential of AI in healthcare.

4. Research Significance and Impact:
   This research offers valuable insights into the current landscape of AI in health and medicine, highlighting both the advancements made and the hurdles that remain. By emphasizing the need for ongoing research and development, particularly in addressing ethical concerns and enhancing data diversity, the study advocates for a future where AI can be effectively utilized to improve patient outcomes universally. The implications of this research extend beyond technological advancements; they call for a concerted effort to ensure that AI applications in healthcare are developed responsibly and inclusively, ultimately leading to more equitable healthcare solutions for diverse populations worldwide.

References

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3. Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. - Jasjit S Suri;Sudip Paul;Maheshrao A Maindarkar;Anudeep Puvvula;Sanjay Saxena;Luca Saba;Monika Turk;John R Laird;Narendra N Khanna;Klaudija Viskovic;Inder M Singh;Mannudeep Kalra;Padukode R Krishnan;Amer Johri;Kosmas I Paraskevas - Metabolites (2022)
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8. The state of the art for artificial intelligence in lung digital pathology. - Vidya Sankar Viswanathan;Paula Toro;Germán Corredor;Sanjay Mukhopadhyay;Anant Madabhushi - The Journal of pathology (2022)
9. A rapid review of machine learning approaches for telemedicine in the scope of COVID-19. - Luana Carine Schünke;Blanda Mello;Cristiano André da Costa;Rodolfo Stoffel Antunes;Sandro José Rigo;Gabriel de Oliveira Ramos;Rodrigo da Rosa Righi;Juliana Nichterwitz Scherer;Bruna Donida - Artificial intelligence in medicine (2022)
10. Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis. - Alberto Eugenio Tozzi;Francesco Fabozzi;Megan Eckley;Ileana Croci;Vito Andrea Dell'Anna;Erica Colantonio;Angela Mastronuzzi - Frontiers in oncology (2022)

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