Artificial intelligence has helped ensure a data-driven methodology for evaluating eligibility criteria for clinical trials, facilitating more inclusive trial design.
Artificial intelligence (AI) and machine-based learning (MBL) have brought renewed hope to certain existential crises facing the health care field today, such as health (in)equity and ever-increasing clinical data and its effective application in precision medicine practices. Today, AI and MBL can use clinical data available to more accurately predict which cancer treatments a patient may respond best to, and AI has helped ensure a data-driven methodology for evaluating eligibility criteria for clinical trials, facilitating more inclusive trial design.
On page 37 authors Brian Cox, MBA, MSSF, FACHE; Alberto Coustasse-Hencke, DrPH, MD, MBA, MPH; Monisha Gupta, PhD; and Craig Kimble, PharmD, MBA, MS, BCACP, discuss how AI is transforming cancer research and treatment by helping to identify intricate patterns in medical data and providing quantitative evaluations of clinical conditions. However, the authors note that challenges remain in the safe use of AI, such as concerns regarding the regulation of AI tools. Specifically, MBL algorithms continue to change as they analyze greater and greater amounts of data, raising concerns around how medical devices or algorithms using AI that have received FDA approvals will be monitored by the FDA on a more ongoing basis.
On page 32, authors Avital Gaziel, PhD, and Yelena Lapidot, PhD, discuss how patient-centric AI decision support systems have started to offer a promising avenue for addressing gaps in health equity in cancer care. Specifically, patient-oriented AI-based platforms can help identify barriers patients with cancer encounter when considering treatment, which can in turn help keep health care and support organizations informed so they can support patients in the process of addressing these issues as they arise.
Additionally, in the cover story on page 22, authors Amir Ali, PharmD, BCOP; Merry Ann Sta Maria, PharmD candidate; Melissa Martinez, PharmD candidate; and Jose Tinajero, PharmD, BCOP, discuss challenges around dosing for novel oncology drug combinations to treat advanced or metastatic cancer. Notably, the authors explain that although advancements in precision medicine are effectively expanding the role of pharmacists, they are also revealing gaps in pharmacy education in the process.
On page 52 authors Yuxi Lei, PharmD candidate; Rebecca Pokorny, PharmD, BCPS, BCOP; and Luisa Giannangelo, MBA, RPh, discuss National Comprehensive Cancer Network panel updates to recommendations for adjuvant therapy in resectable non–small cell lung cancer (NSCLC). According to the authors, the advancements in adjuvant immunotherapy and biomarker testing represent a substantial step forward in the adjuvant therapy space for resectable NSCLC.
AI and MBL will remain critical areas of growth in cancer treatment and care, with seemingly endless applications and uses. Currently, the potential before us to revolutionize the oncology field seems limited only by our imaginations.