Evidence map›Paper›PMID 31209850›Full record

ArticleCancer treatment and research2019

Artificial Intelligence and Personalized Medicine.

Nicholas J Schork

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer treatment and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
143citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

143 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Review
  19. Article
  20. Review

83 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nicholas J SchorkDepartment of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA. nschork@tgen.org.

Funding

Systems BiologyU19AG023122 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI FRED H GAGE · 2004 to 2026
$102.6M
Integrative Resource to Develop Translational Strategies to Promote LongevityU24AG051129 · NIA · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI CUMMINGS, STEVEN RON, EVANS, DANIEL SPENCER · 2015 to 2019
$5.3M
Integrative Omics to enhance therapeutics development for healthy agingUH2AG064706 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI MILLER, RICHARD A, SCHORK, NICHOLAS JOSEPH · 2019 to 2020
$1.4M
NIA NIH HHS UH2 AG064706
6 · The paper itself

Abstract

The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,'  experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition,  the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for  treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.

Indexed as

Artificial IntelligencePrecision MedicineHumansArtificial intelligenceBig dataClinical trialsPersonalized medicine

Identifiers

PMID31209850
PMCPMC7580505

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.