Evidence map›Paper›PMID 40559258›Full record

ReviewNanomaterials (Basel, Switzerland)2025

Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.

Nargish Parvin, Sang Woo Joo, Jae Hak Jung, Tapas K Mandal

Erratum issuedAbstract readReview
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  7. Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Nargish ParvinSchool of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0002-1209-1507
Sang Woo JooSchool of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0002-3694-6813
Jae Hak JungSchool of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Tapas K MandalSchool of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.ORCID 0000-0003-2615-8618

Funding

NRFK NRF-2019R1A5A8080290
6 · The paper itself

Abstract

Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, and regenerative therapies. Advanced tools like AlphaFold have significantly improved protein structure prediction, enabling the creation of biomaterials with enhanced biological compatibility. In diagnostics, AI systems synthesize multimodal inputs combining imaging, molecular markers, and clinical data-to improve diagnostic precision and support early disease detection. For precision medicine, AI integrates data from wearable technologies, continuous monitoring systems, and individualized health profiles to inform targeted therapeutic strategies. Despite its promise, the integration of AI into clinical practice presents challenges such as ensuring data security, meeting regulatory standards, and promoting algorithmic transparency. Addressing ethical issues including bias and equitable access remains critical. Nonetheless, the convergence of AI and biotechnology continues to shape a future where healthcare is more predictive, personalized, and responsive.

Indexed as

AlphaFoldbiomaterials sciencemultimodal AIregenerative medicinewearable healthcare

Identifiers

PMID40559258
PMCPMC12195918

What Socratic holds

Textmetadata
LicenceCC BY
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.