Evidence map›Paper›PMID 38315404›Full record

ArticlePharmaceutical medicine2024

Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases.

Giulio Napolitano, Canan Has, Anne Schwerk, Jui-Hung Yuan, Carsten Ullrich

Abstract read
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In one paragraph

Article in Pharmaceutical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

5 authors.

Giulio NapolitanoCentogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany. gnapolitano01@qub.ac.uk.ORCID 0000-0002-6842-7575
Canan HasCentogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany.ORCID 0000-0003-2355-3727
Anne SchwerkBeriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Jui-Hung YuanBeriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Carsten UllrichBeriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany.ORCID 0000-0003-2826-9821

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.

Indexed as

Artificial IntelligenceRare DiseasesDrug DevelopmentDrug DiscoveryHumans

Identifiers

PMID38315404

What OpenQuestion holds

Textmetadata
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.