Role of Artificial Intelligence (AI) Enabled Tools in Healthcare and Drug Discovery Process: A Narrative Review

Introduction

Artificial intelligence (AI) refers to computational systems capable of performing tasks that traditionally require human intelligence, including learning, pattern recognition, reasoning, and predictive decision-making. In healthcare and pharmaceutical sciences, AI primarily encompasses machine learning (ML), deep learning (DL), and natural language processing (NLP), enabling the analysis of complex, high-dimensional biomedical data. The rapid expansion of electronic health records, medical imaging repositories, genomic databases, and real-world evidence, together with advances in computing infrastructure, has accelerated the adoption of AI-enabled tools in clinical practice and biomedical research.^[1,2] These tools increasingly support clinicians, pharmacists, and researchers in clinical documentation, diagnostic interpretation, medication information retrieval, data analysis, and patient education, thereby enhancing efficiency and evidence-informed decision-making.^[1,3]

Recent advances in large language models (LLMs), such as ChatGPT, have expanded the scope of AI applications across healthcare systems. LLMs demonstrate strong capabilities in medical knowledge synthesis, information retrieval, patient communication, academic support, and research assistance.^[4,5] In addition to clinical decision support, AI-enabled tools are being explored in pharmacovigilance for adverse drug reaction reporting, in pharmacy practice for medication safety and drug-drug interaction (DDI) screening, and in medical education for teaching, assessment, and competency-based training. However, limitations related to contextual understanding, potential inaccuracies, lack of explainability, and dependence on training data quality restrict their use as autonomous systems. AI has also gained importance in drug discovery and development, a process traditionally characterised by long timelines, high costs, and high attrition rates. AI-driven approaches support target identification, virtual screening, lead optimisation, toxicity prediction, and clinical trial design, improving efficiency across the drug development pipeline.

Despite the expanding use of AI, existing literature largely examines AI applications in isolated domains such as clinical decision support or drug discovery, with limited integrated discussion across pharmacovigilance, pharmacy practice, medical education, and research.^[4] Most studies emphasise technical performance or ethical frameworks, while evidence regarding real-world implementation, human-in-the-loop oversight, and cross-disciplinary impact remains fragmented.^[5] This narrative review aims to critically synthesise current evidence on AI-enabled tools across healthcare delivery, pharmacovigilance, pharmacy practice, medical education, and drug discovery, while highlighting limitations, ethical considerations, and governance requirements for responsible integration.

Review

Role of AI Tools in Drug Discovery and Development

From a pharmaceutical perspective, conventional drug discovery is a resource-intensive and time-consuming process, often extending over a decade with a high rate of candidate attrition during preclinical and clinical development. These challenges have driven the integration of AI tools to improve efficiency, reduce costs, and support rational decision-making across discovery pipelines. Recent advances demonstrate that AI-driven models can effectively assist in target identification, molecular modelling, virtual screening, and lead optimisation, thereby addressing key bottlenecks in traditional drug discovery workflows^[23,24] [Table 1].

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Table 1.

Applications of artificial intelligence (AI) tools across various stages of the drug discovery and development

Drug Discovery Stage	Role of AI Tools	Representative AI Tools/Models	References
Clinical trial design	Aid in patient stratification, endpoint prediction, trial simulation, and protocol optimisation.	ML-based clinical analytics model	[6]
Lead optimisation	AI models optimise potency, selectivity, and ADMET profiles to improve lead candidates.	Graph-based deep learning models, Reinforcement Learning models, ADMET prediction models	[23,24]
Target identification	AI-driven integration of genomics, proteomics, and transcriptomics to prioritise disease-relevant targets, and protein structure prediction to support target characterisation.	AlphaFold, ML-based target prediction,	[25]
Target validation	Machine learning models validate disease-target associations using biological networks and clinical datasets.	Network medicine models, Interactome-based systems biology tools	[26]
Hit identification	AI-powered virtual screening and de novo molecular design to accelerate hit discovery from large chemical libraries.	Machine learning and deep learning models	[23,27]
Preclinical development	Predict compound toxicity to identify safety liabilities early and reduce late-stage failures.	DeepTox: Deep neural network-based toxicity prediction	[28]
Knowledge mining & decision support	LLMs extract, summarise, and interpret biomedical literature and real-world evidence for decision support.	LLM model	[29]

Note: ADMET: Absorption, distribution, metabolism, excretion, and toxicity; LLM: Large language model; ML: Machine learning.

Limitation

Key limitations of AI tools are limited contextual understanding and outdated data, which can lead to irrelevant or repetitive responses.^[30] Ethical concerns arise when AI systems fail to prioritise patient welfare, as inadequate training or limited data can lead to inappropriate or uninformed responses [Table 2].

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Table 2.

Ethical considerations of AI tools in healthcare and the drug discovery process

Domain	Sub-domain	Key Ethical Issues	Concerns/Examples	Mitigation Strategies	References
Healthcare	Medical education	Academic integrity, bias, and reliability	Plagiarism, inaccurate content, and learner dependency	Supervised use, integrity policies, and curriculum governance	[9,11,13]
Healthcare	Research assistance	Fabrication, reproducibility, transparency	Hallucinated or fabricated references, non-verifiable outputs	Mandatory human validation, disclosure of AI use	[20,31]
Healthcare	Pharmacovigilance	Signal validity, accountability, and data reliability	False or incomplete ADR signals, lack of causality assessment	Human-led causality evaluation, regulatory verification	[21]
Healthcare	Pharmacy practice	Medication safety, bias, and professional responsibility	Incorrect drug-drug interaction alerts, patient over-reliance on AI	Pharmacist verification, AI as an advisory tool only	[22]
Healthcare	Clinical practice	Privacy, accountability, transparency	Misuse or exposure of patient data, misdiagnosis without oversight, and a lack of explainability	Anonymisation, secure governance, human-in-the-loop verification, disclosure	[21,32,33]
Drug Discovery	AI-assisted discovery & development	Data privacy & bias, transparency & explainability, accountability & validation	Biased training data, non-interpretable prioritisation, inequitable impact	Data governance, interpretable models, human validation, bias auditing	[23,33–35]

Conclusion

AI-enabled tools, like ChatGPT, are increasingly influencing healthcare delivery by supporting clinical decision-making and knowledge synthesis. In pharmacy practice and pharmacovigilance, an AI tool assists in DDI screening, medication safety, and ADR signal awareness, whereas causality assessment and regulatory decisions remain clinician-led. Similarly, in medical education and research, AI tools support learning, assessment preparation, literature synthesis, and scientific writing; however, supervised use is essential due to concerns regarding accuracy, bias, and academic integrity. AI-driven approaches enhance efficiency across drug discovery pipelines. However, current applications function primarily as human-in-the-loop decision-support systems, not autonomous clinical or regulatory authorities. Ethical challenges related to data privacy, bias, transparency, and accountability remain critical considerations. Although several AI-assisted drug candidates have entered early-phase clinical trials, comprehensive preclinical and clinical validation continues to be the major bottleneck. Continued multidisciplinary collaboration, rigorous validation, and regulatory oversight are essential for the responsible and effective integration of AI into healthcare and drug development.