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Artificial Intelligence in Healthcare and Education · Journal article
Current Drug Discovery Technologies · July 28, 2026
A consensus or society position rather than new primary data.
This narrative review surveys current and emerging applications of AI across pharma R&D, spanning drug discovery, formulation, preclinical testing, and intelligent diagnostics. The source documents broad uptake by leading companies and claims of time and accuracy gains but does not report quantified comparative outcomes, primary endpoint data, or controlled evidence that would support practice-changing claims.
Narrative review. Pharmaceutical companies and clinical diagnostics applications across oncology, cardiology, and other medical specialties; no specific patient populations quantified..
AI application areas identified include search for new pharmacologically active substances, formulation and production technology, preclinical trials, and biomarker discovery AI in drug discovery purportedly reduces time to identify candidate molecules through rapid biotarget identification, virtual screening, and optimization based on predictive pharmacokinetic and toxicological data AI-based intelligent diagnostics claimed to demonstrate high accuracy and time efficiency in oncology, cardiology, and other medical areas compared to conventional diagnostic methods
No data on safety, adverse events, or failure rates of AI systems in the applications described. AI application areas identified include search for new pharmacologically active substances, formulation and production technology, preclinical trials, and biomarker discovery
This review provides a broad landscape and organizational framework for clinicians and researchers considering AI deployment in pharma and diagnostics, but does not establish the magnitude of clinical benefit or support specific operational decisions without access to the underlying examples and evidence cited.
A narrative review synthesizing current applications and best practices of AI in pharma R&D, offering expert perspective on opportunities and implementation challenges rather than primary evidence of efficacy.
As stated by the source record.
This review provides a broad landscape and organizational framework for clinicians and researchers considering AI deployment in pharma and diagnostics, but does not establish the magnitude of clinical benefit or support specific operational decisions without access to the underlying examples and evidence cited.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
The review is devoted to the use of artificial intelligence (AI) in scientific research and development to create new or repurpose authorized drug products, as well as to the use of AI-based solutions to discover new biomarkers and shorten the time to diagnosis of various diseases. The following areas of AI application are considered: the search for new pharmacologically active substances, the development of formulations and drug production technology, preclinical trials, and intelligent diagnostics (identification of new biomarkers; development of software products to interpret research results and increase diagnostic accuracy). Examples of AI use by leading pharmaceutical companies and a list of the most popular AI models in drug development are provided. The revolutionary contribution of AI in drug discovery lies in reducing the time to identify new drug candidate molecules by more rapidly identifying potential biotargets, performing virtual screening, optimizing promising candidates based on predictive data on pharmacokinetic and toxicological profiles, and searching for the optimal way to synthesize potential drugs. In addition, another area of AI application is the development of drug-delivery devices and systems that improve patient compliance and usability. This paper presents examples of AI use in intelligent diagnostics that prove their high accuracy and time efficiency compared to conventional methods of diagnostics, risk assessment, and prognosis in oncology, cardiology, and other areas of medicine. The implementation of AI technologies in medicine is intensifying, raising questions of ethics, the quality and adequacy of data, the effectiveness and safety of results for patients, personnel competence and readiness for change, as well as issues related to intellectual property rights.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.