Evidence map›Paper›PMID 39660914›Full record

ReviewRegenerative medicine2024

Artificial intelligence powers regenerative medicine into predictive realm.

Armin Garmany, Andre Terzic

Abstract readReview
In one paragraph

Review in Regenerative medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
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

2 authors.

Armin GarmanyMarriott Heart Disease Research Program, Department of Cardiovascular Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0002-5518-8544
Andre TerzicMarriott Heart Disease Research Program, Department of Cardiovascular Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-9210-009X

Funding

MSTP at Mayo Clinic RochesterT32GM145408 · NIGMS · MAYO CLINIC ROCHESTER · PI SCOTT H KAUFMANN, LISA A SCHIMMENTI · 2023 to 2026
$4.7M
ENHANCING CARDIOPOIETIC REGENERATION FOR ISCHEMIC CARDIOMYOPATHYR01HL134664 · NHLBI · MAYO CLINIC ROCHESTER · PI TERZIC, ANDRE · 2018 to 2021
$1.6M
NHLBI NIH HHS R01 HL134664NIGMS NIH HHS T32 GM145408
6 · The paper itself

Abstract

The expanding regenerative medicine toolkit is reaching a record number of lives. There is a pressing need to enhance the precision, efficiency, and effectiveness of regenerative approaches and achieve reliable outcomes. While regenerative medicine has relied on an empiric paradigm, availability of big data along with advances in informatics and artificial intelligence offer the opportunity to inform the next generation of regenerative sciences along the discovery, translation, and application pathway. Artificial intelligence can streamline discovery and development of optimized biotherapeutics by aiding in the interpretation of readouts associated with optimal repair outcomes. In advanced biomanufacturing, artificial intelligence holds potential in ensuring quality control and assuring scalability through automated monitoring of process-critical variables mandatory for product consistency. In practice application, artificial intelligence can guide clinical trial design, patient selection, delivery strategies, and outcome assessment. As artificial intelligence transforms the regenerative horizon, caution is necessary to reduce bias, ensure generalizability, and mitigate ethical concerns with the goal of equitable access for patients and populations.

Indexed as

Artificial IntelligenceRegenerative MedicineHumansartificial intelligencehealthcaremachine learningmanufacturingregenerationstem cellsystems biologytissue engineering

Identifiers

PMID39660914
PMCPMC11703382

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.