What does this research mean for the field?

Artificial intelligence is successfully transitioning into an essential clinical 'co-pilot' capable of expert-level diagnostics and workflow optimization, though autonomous deployment remains hindered by 'black box' architectures and data hallucinations. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Evaluate the clinical, therapeutic, and administrative applications of artificial intelligence in healthcare alongside its technical, systemic, and ethical implementation challenges.

August 30, 2026International Journal of Innovative Technologies in Social ScienceOpen Access

Artificial Intelligence in Modern Medicine: A Comprehensive Review of Current Applications, Challenges, and Future Perspectives

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Authors

ASAleksandra StańczykUniversity of Warmia and Mazury in Olsztyn KHKinga HaduchKrakow Cardiovascular Research Institute ZMZuzanna MichalskaUniversity of Warmia and Mazury in Olsztyn

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Implication

Structured review demonstrates expert-level clinical performance and operational gains from artificial intelligence in healthcare workflows, highlighting persistent challenges in algorithmic...

Key Points

Evaluate the clinical, therapeutic, and administrative applications of artificial intelligence in healthcare alongside its technical, systemic, and ethical implementation challenges.
Conducted a structured review utilizing a hybrid framework combining a multi-decade bibliometric trend perspective.
Synthesized findings from 21 landmark publications, clinical trials, and meta-analyses published in high-impact journals.
Artificial intelligence models achieved expert-level accuracy in clinical knowledge retrieval and spatiotemporal diagnostics, with AlphaFold 3 advancing computational therapeutics via all-atom biomolecular interaction prediction.
Ambient AI scribes substantially mitigated physician administrative burden and burnout by automating documentation workflows.
Autonomous institutional deployment remains constrained by large language model hallucinations and the opaque, unexplainable nature of deep learning architectures.

Cite This Study

Stańczyk et al. (2026) studied this question.

synapsesocial.com/papers/6a93f00a6c1a8fb52e79c119 https://doi.org/10.31435/ijitss.3(51).2026.6205

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Implication

Structured review demonstrates expert-level clinical performance and operational gains from artificial intelligence in healthcare workflows, highlighting persistent challenges in algorithmic...

Artificial Intelligence in Modern Medicine: A Comprehensive Review of Current Applications, Challenges, and Future Perspectives

Discussion

Member takes

Implication

Key Points

Cite This Study

Artificial Intelligence in Modern Medicine: A Comprehensive Review of Current Applications, Challenges, and Future Perspectives

Discussion

Member takes

Implication

Key Points

Cite This Study

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