Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology

Abstract

Objective: Artificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it. Methods and materials: Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made. Results: The article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it’s the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future. Conclusion: AI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians.

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

“…Some important uses of ML applications in clinical practice include: provision of up-to-date information for reducing diagnostic and therapeutic errors, real time inferences, health risk alerts, and health outcome predictions 11,12 . Though there is substantial literature of AI and ML in healthcare research, most of the research focuses in the fields of Cancer, Neurology and Cardiology 11,[13][14][15][16][17][18][19][20][21] . In addition, the literature lacks successful applications of ML that deal with complex medical diagnostic fields like Hematology 22 .…”

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

“…Applications such as computer-aided diagnosis and detection, image registration, image reconstruction, outcome prediction, etc., were not discussed but AI is also appearing in these parts of the radiotherapy workflow, see e.g. [6,7,9,10,12,[143][144][145]. For future perspectives of AI in RT, we refer the reader to [11,146,147].…”

“…AI is characterized as a collection of algorithms that perform tasks correlated with human thinking or intelligence [4] with machine learning (ML) and deep learning (DL) as subdomains [5]. Several review papers have been published on the use of AI, ML and DL in radiotherapy [6][7][8][9][10][11][12]. However, not much is written on clinical implementation of these new techniques [13,14].…”

Digital Patient-Reported Outcome Measures for Monitoring of Patients on Cancer Treatment: Cross-sectional Questionnaire Study

“…Video consultations to enable remote review of patients have been shown to be safe and effective when used appropriately [40], and their use has expanded rapidly in response to the COVID-19 pandemic [41]. Other technological solutions that seek to improve the efficiency of a number of aspects of oncology work include artificial intelligence applied to radiomics, such as breast screening interpretation [42], and streamlining RT workflows, such as through auto-contouring during RT outlining and voxel-based dose prediction approaches to refine the treatment planning process [43]. Thus, digital technology, including electronic PROMs, looks set to have a significant impact on oncology practice.…”