Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Article . 2023
License: CC BY
Data sources: ZENODO
versions View all 2 versions
addClaim

AI-Driven Breakthroughs in Healthcare: Google Health's Advances and the Future of Medical AI

Authors: Dr.A.Shaji George; A.S.Hovan George; Aakifa Shahul; Dr.T.Baskar;

AI-Driven Breakthroughs in Healthcare: Google Health's Advances and the Future of Medical AI

Abstract

Artificial intelligence (AI) has emerged as a powerful tool with the potential to transform various aspects of healthcare. Over the past five years, Google Health has been at the forefront of developing and implementing AI-driven solutions to address numerous challenges in the healthcare industry. This paper presents a comprehensive review of Google Health's progress in harnessing AI to improve healthcare outcomes, with a particular emphasis on their latest conversational AI systems, MedPALM and MedPALM 2, and the potential applications and limitations of these systems. The review begins with an overview of AI's impact on healthcare, highlighting the numerous applications where AI has proven to be beneficial in augmenting the abilities of healthcare professionals and enabling the discovery of new medical knowledge. This is followed by an in-depth analysis of Google Health's key AI innovations, including advancements in breast cancer detection, skin condition identification, genomic sequencing, and the discovery of a tissue morphology feature that predicts colorectal cancer patient survival. The paper then delves into the development, tuning, and performance of MedPALM, a large language model designed to provide high-quality and authoritative answers to medical questions. MedPALM's achievements in surpassing the pass mark on U.S. medical licensing exams are discussed, along with an examination of the evaluation process of MedPALM's answers in comparison to real clinicians. Building on the success of MedPALM, the paper introduces MedPALM 2, a more advanced and improved AI system that boasts impressive performance on medical exam benchmarks, including Indian medical exams. The potential real-world applications and role of MedPALM 2 as a building block for advanced natural language processing in healthcare are explored, emphasizing the tremendous potential of this technology in the field. Lastly, the review addresses the challenges and limitations of AI in healthcare, including the importance of empathy, compassion, addressing bias, and ethical considerations. The paper stresses the need for responsible innovation and the inclusion of diverse experiences, perspectives, and expertise when developing AI systems for healthcare applications. To wrap up, this paper delves deep into Google Health's AI-driven advances in healthcare and its vast potential to transform the sector. However, we must not forget that significant obstacles remain before we can responsibly deploy these technologies ethically.

Keywords

Artificial intelligence (AI), Google Health, Healthcare outcomes, Conversational AI systems, MedPALM, MedPALM 2, Medical knowledge discovery, AI-driven healthcare innovations, Natural language processing (NLP), AI challenges and limitations.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 47
    download downloads 33
  • 47
    views
    33
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
3
Top 10%
Average
Average
47
33
Green
Related to Research communities
Cancer Research