TLDR: Artificial intelligence is profoundly transforming oncology by improving the efficiency and precision of clinical trials, optimizing pharmacy practices, and enabling highly personalized cancer treatments. AI-driven tools are streamlining patient recruitment, refining study designs, and enhancing data analysis, leading to faster drug development and better patient outcomes.
Artificial intelligence (AI) is rapidly emerging as a transformative force in oncology, fundamentally reshaping how clinical trials are conducted, how pharmacy operations are managed, and how cancer treatments are personalized for patients. This technological revolution promises to accelerate drug development, enhance patient outcomes, and optimize the entire oncology workflow.
In the realm of clinical trials, AI-driven tools are proving instrumental in improving study design, patient recruitment, and data analysis. Machine learning (ML) algorithms can swiftly and accurately match patients to suitable trials, refine trial designs, and uncover deeper patterns within real-world data (RWD), making trials more precise and efficient. AI algorithms can also monitor new trial data to identify safety or effectiveness signals far more rapidly than traditional methods, leading to faster, more cost-effective, and precise clinical trials. Computational pathology is also adopting AI-driven diagnostics, ensuring consistency and clinical trust in trial endpoints and data pipelines.
The impact of AI extends significantly into oncology pharmacy practice. AI is empowering healthcare providers to make more informed decisions, particularly in selecting personalized therapies that align with precision medicine approaches. These decisions are based on a patient’s lifestyle, genetic profile, and clinical presentation. Furthermore, AI is being utilized for remote patient monitoring to track medication adherence and management. AI can also automate routine tasks such as scheduling, coding, or data entry, thereby freeing up clinicians to dedicate more time to direct patient care. Eugene Przespolewski, PharmD, BCOP, DPLA, a clinical pharmacy specialist at Roswell Park Comprehensive Cancer Center, highlighted at the 2025 Oncology Pharmacists Connect (OPC) meeting that AI is “certainly the next frontier in medicine,” emphasizing its role in optimizing care models and addressing challenges like drug shortages.
AI’s influence on oncology is not merely theoretical; it is already yielding tangible results. Deep learning models have achieved dermatologist-level accuracy in identifying skin cancer through imaging algorithms. In another notable example, deep learning has demonstrated the ability to predict microsatellite instability directly from histology slides in gastrointestinal cancers, showcasing its power in advancing oncologic diagnostics. A 2021 systematic review confirmed that deep learning algorithms exhibit high diagnostic accuracy across various imaging modalities, frequently matching or surpassing human expert performance in medical imaging tasks within oncology.
While the integration of AI presents challenges related to new system integration, data quality, and ethical responsibilities, its potential in oncology is undeniable. Experts anticipate that 2025 will be a “turning point,” with the first AI-discovered or AI-designed therapeutic oncology candidates potentially entering first-in-human trials, signaling a paradigm shift in therapy development. Companies like AstraZeneca and Pfizer are already leveraging AI’s computational power to design better trials, predict the efficacy and safety profiles of molecules, and synthesize vast multi-omic information for a more comprehensive understanding of complex cancers. As Arun Krishna, head of U.S. oncology at AstraZeneca, stated, “AI enables companies to move from intuition-driven to data-driven drug development.” The FDA has also recognized AI’s value, announcing plans to phase out animal testing for certain therapies in favor of “AI-based computational models” and human organoid lab models, aiming for safer, faster, and more reliable treatments while reducing R&D costs.
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Despite the immense promise, challenges remain, particularly concerning patient trust. While younger patients tend to be less trusting of AI, older patients show more openness. AI is more readily trusted for low-risk recommendations like diet or exercise, but hesitation remains for high-stakes decisions such as diagnoses or treatment plans. Ultimately, the success of AI tools will depend on their seamless integration into clinical and operational workflows, rather than solely on the sophistication of their algorithms.
