Overview of the PRN

The Hematology/Oncology PRN, founded in 1994, serves as a hub for pharmacists and students both nationally and internationally who are passionate about oncology. This network of over 500 members facilitates knowledge exchange as well as professional connections. It offers a platform for pharmacists and learners to engage in discussions on scientific advances, educational initiatives, and policy matters that influence their field. The PRN also acts as a valuable resource, providing expert insights to the wider community. Three key committees – Membership & Operations, Scholarship, and Communications – together with dedicated members drive these objectives forward, fostering collaboration and growth within the specialty.

All students, residents, and fellows are encouraged to follow the PRN on social media: X/Twitter @HemOnc_ACCP and Instagram @ACCPHEMONCPRN.

PRN Leadership

Chair: Farah Raheem, Pharm.D., BCOP

Chair-Elect: Nikola Paulic, Pharm.D., BCOP

Secretary-Treasurer: Shawn P. Griffin, Pharm.D., BCOP

Board Liaison: Candice L. Garwood, Pharm.D., FCCP, BCPS

Opportunities and Resources for Student, Resident, and Fellow PRN Members

Residents and fellows have many networking, scholarship, professional, and leadership opportunities to participate in and benefit from as members of the Hematology/Oncology PRN.

All PRN learner members gain access to the PRN’s email list to stay up-to-date with the PRN’s biannual newsletter and clinical discussions. Pharmacist members enjoy networking with PRN learners at the annual business meeting of the ACCP Annual Meeting – everyone is invited to attend!

Each fall, learners are encouraged to apply for positions on one of three committees. This involvement not only provides leadership experience but also ensures that resident, fellow, and graduate student perspectives are incorporated into the PRN’s activities. The PRN is committed to nurturing the next generation of oncology pharmacists, offering mentored opportunities to support learners’ growth.

Beyond ACCP’s standard offerings, the Hematology/Oncology PRN provides additional avenues for both committee members and general learner members to engage and develop in the field. These opportunities include:

• Presenting monthly journal clubs (the learner is paired with an oncology pharmacist mentor)
• Working on peer-reviewed scholarly publications (e.g., original research, review articles, white papers, and PRN statements)
• Participating in annual virtual rotations each December/January
• Contributing to PRN publications, including the biannual PRN newsletter and informative social media posts (the learner is paired with an oncology pharmacist mentor)
• Supporting and brainstorming ideas for new PRN initiatives and benefiting from such initiatives (e.g., BCOP study groups, careers in oncology webinar, leading/participating in multicenter research projects, collaborations with industry)
• Eligibility for the following awards: ACCP Hematology/Oncology PRN Learner Engagement Award (established in 2023) and the PRN Scholar Travel Award (new in 2024!)

Current Issue Important to the Hematology/Oncology PRN Members: The Potential and Challenges of Integrating Artificial Intelligence into Oncology

Artificial intelligence (AI) is rapidly transforming various aspects of health care, and oncology is no exception.¹ As we stand on the cusp of a new era in cancer care, it’s crucial to examine both the immense potential and the significant challenges that come with integrating AI into oncology practice. When used appropriately, AI could revolutionize cancer diagnosis, treatment planning, and drug development. Challenges must be overcome for successful implementation of AI in oncology practice.

Potential of AI in Oncology

Enhanced Diagnostic Accuracy:

AI algorithms, particularly deep learning models, have shown remarkable ability in analyzing medical images.² In oncology, this could lead to earlier and more accurate detection of cancers, potentially improving patient outcomes. For instance, AI systems can analyze mammograms, CT scans, and pathology slides with a level of detail and consistency that may surpass human capabilities.^3,4

Patient Treatment Plans:

By analyzing vast amounts of patient data, including genetic information, treatment histories, and outcomes, AI can help oncologists develop more personalized treatment plans. This could lead to more effective therapies with fewer adverse effects, given that treatments are tailored to individual patient profiles.

Drug Discovery and Development:

AI has the potential to accelerate the drug discovery process by predicting drug-target interactions, optimizing molecular structures, and identifying potential candidates for clinical trials.⁵ This could lead to faster development of new cancer therapies and repurposing of existing drugs for cancer treatment.

Predictive Analytics:

AI models can analyze patterns in patient data to predict treatment outcomes, potential complications, or the risk of cancer recurrence.^6,7 This predictive capability could help in making more informed decisions about treatment strategies and follow-up care.

Clinical Trial Matching:

AI algorithms can efficiently match patients with appropriate clinical trials on the basis of their specific cancer type, genetic profile, and other relevant factors.⁸ This could accelerate research and provide patients with access to cutting-edge treatments.

Challenges in Integrating AI

Data Quality and Standardization:

AI models require large amounts of high-quality standardized data for training and validation. However, medical data can be inconsistent, biased, or incomplete.⁹ Establishing standardized data collection and sharing practices across institutions is a significant challenge.

Ethical and Privacy Concerns:

The use of patient data for AI training raises important ethical and privacy concerns. Ensuring patient confidentiality, obtaining informed consent, and addressing potential biases in AI algorithms are crucial challenges that need to be addressed.¹⁰

Regulatory Hurdles:

The rapid pace of AI development often outstrips the regulatory framework. Establishing clear guidelines for the development, validation, and implementation of AI tools in oncology is essential to ensure patient safety and efficacy.^7,11

Integration into Existing Workflows:

Implementing AI systems into existing clinical workflows can be challenging. This requires significant changes in infrastructure, training for health care professionals, and overcoming potential resistance to new technologies.¹²

Interpretability and Trust:

Many AI algorithms, particularly deep learning models, operate as “black boxes,” making it difficult to understand how they arrive at their conclusions.¹³ The term black boxes refers to the neural network-based approach of deep learning–based AI that relies on hidden layers of data interactions and algorithms.⁷ Building trust in AI systems among health care professionals and patients is crucial for widespread adoption.

Validation and Real-world Performance:

Although AI models may perform well in controlled research settings, their effectiveness in real-world clinical scenarios needs to be rigorously validated.⁷ Ensuring that AI tools maintain their performance across diverse patient populations and clinical settings is a significant challenge.

Integration of AI into oncology holds great promise for improving cancer diagnosis, treatment, and drug development. However, realizing this potential requires addressing significant challenges related to data management, bias, ethics, regulation, and integration in practice and clinical trials. As oncology pharmacists, we play a crucial role in navigating these challenges and helping to shape the responsible implementation of AI into cancer care. By staying informed about AI developments, participating in interdisciplinary collaborations, and advocating for patient-centric AI solutions, we can contribute to a future where AI enhances rather than replaces human expertise in oncology.

Prepared by: Farah Raheem, Pharm.D., BCOP

References

1. Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 2019;69:127-57.
2. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8.
3. Huang S, Yang J, Fong S, et al. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett 2020;28:471:61-71.
4. Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health 2019;1:e271-e297.
5. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019;18:463-77.
6. Grewal JK, Tessier-Cloutier B, Jones M, et al. Application of a neural network whole transcriptome–based pan-cancer method for diagnosis of primary and metastatic cancers. JAMA Netw Open 2019;2:e192597.
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8. Haddad T, Helgeson JM, Pomerleau KE, et al. Accuracy of an artificial intelligence system for cancer clinical trial eligibility screening: retrospective pilot study. JMIR Med Inform 2021;9:e27767.
9. Council for Affordable Quality Healthcare (CAQH). Defining the Provider Data Dilemma, 2016: Challenges, Opportunities, and Call for Industry Collaboration. CAQH, 2016. Available at www.caqh.org/sites/default/files/explorations/defining-provider-data-white-paper.pdf.
10. Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA 2019;322:2377-8.
11. Gunkel DJ. Mind the gap: responsible robotics and the problem of responsibility. Ethics Inf Technol 2020;22:307-20.
12. Chua IS, Gaziel-Yablowitz M, Korach ZT, et al. Artificial intelligence in oncology: path to implementation. Cancer Med 2021;10:4138-49.
13. Kundu S. AI in medicine must be explainable. Nat Med 2021;27:1328.