Evidence map›Paper›PMID 39152194›Full record

ReviewScientific reports2024

Innovation and challenges of artificial intelligence technology in personalized healthcare.

Yu-Hao Li, Yu-Lin Li, Mu-Yang Wei, Guang-Yu Li

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
118citing papers in PubMed, 13 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

118 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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  6. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
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58 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

4 authors.

Yu-Hao LiInternational School, Beijing University of Posts and Telecommunications, Bei Jing, 100876, China.
Yu-Lin LiDepartment of Ophthalmology, The Second Norman Bethune Hospital of Jilin University, Changchun, 130000, China.
Mu-Yang WeiDepartment of Ophthalmology, The Second Norman Bethune Hospital of Jilin University, Changchun, 130000, China.
Guang-Yu LiDepartment of Ophthalmology, The Second Norman Bethune Hospital of Jilin University, Changchun, 130000, China. liguangyu@aliyun.com.

Funding

National Natural Science Foundation of China 81570864National Natural Science Foundation of China No. 82171053
6 · The paper itself

Abstract

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient.

Indexed as

Artificial IntelligencePrecision MedicineTelemedicineDelivery of Health CareHumansArtificial intelligenceData securityHealthcareRemote patient careVirtual assistant chatbots

Identifiers

PMID39152194
PMCPMC11329630

What OpenQuestion 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 OpenQuestion graph.