Using Artificial Intelligence to Improve the Quality and Safety of Radiation Therapy

A Pilot Study of Simulation-Free Hippocampal-Avoidance Whole Brain Radiation Therapy Using Diagnostic MRI-Based and Online Adaptive Planning

PURPOSE

We aimed to demonstrate the clinical feasibility and safety of simulation-free hippocampal avoidance whole brain radiation therapy (HA-WBRT) in a pilot study (National Clinical Trial 05096286).

METHODS AND MATERIALS

Ten HA-WBRT candidates were enrolled for treatment on a commercially available computed tomography (CT)-guided linear accelerator with online adaptive capabilities. Planning structures were contoured on patient-specific diagnostic magnetic resonance imaging (MRI), which were registered to a CT of similar head shape, obtained from an atlas-based database (AB-CT). These patient-specific diagnostic MRI and AB-CT data sets were used for preplan calculation, using NRG-CC001 constraints. At first fraction, AB-CTs were used as primary data sets and deformed to patient-specific cone beam CTs (CBCT) to give patient-matched density information. Brain, ventricle, and brain stem contours were matched through rigid translation and rotation to the corresponding anatomy on CBCT. Lens, optic nerve, and brain contours were manually edited based on CBCT visualization. Preplans were then reoptimized through online adaptation to create final, simulation-free plans, which were used if they met all objectives. Workflow tasks were timed. In addition, patients underwent CT-simulation to create immobilization devices and for prospective dosimetric comparison of simulation-free and simulation-based plans.

RESULTS

Median time from MRI importation to completion of "preplan" was 1 weekday (range, 1-4). Median on-table workflow duration was 41 minutes (range, 34-70). NRG-CC001 constraints were achieved by 90% of the simulation-free plans. One patient's simulation-free plan failed a planning target volume coverage objective (89% instead of 90% coverage); this was deemed acceptable for first-fraction delivery, with an offline replan used for subsequent fractions. Both simulation-free and simulation CT-based plans otherwise met constraints, without clinically meaningful differences.

CONCLUSIONS

Simulation-free HA-WBRT using online adaptive radiation therapy is feasible, safe, and results in dosimetrically comparable treatment plans to simulation CT-based workflows while providing convenience and time savings for patients.

Sensitivity of automated and manual treatment planning approaches to contouring variation in early-breast cancer treatment

PURPOSE

One of the advantages of integrating automated processes in treatment planning is the reduction of manual planning variability. This study aims to assess whether a deep-learning-based auto-planning solution can also reduce the contouring variation-related impact on the planned dose for early-breast cancer treatment.

METHODS

Auto- and manual plans were optimized for 20 patients using both auto- and manual OARs, including both lungs, right breast, heart, and left-anterior-descending (LAD) artery. Differences in terms of recalculated dose (ΔDrcM,ΔDrcA) and reoptimized dose (ΔDroM,ΔDroA) for manual (M) and auto (A)-plans, were evaluated on manual structures. The correlation between several geometric similarities and dose differences was also explored (Spearman's test).

RESULTS

Auto-contours were found slightly smaller in size than manual contours for right breast and heart and more than twice larger for LAD. Recalculated dose differences were found negligible for both planning approaches except for heart (ΔDrcM=-0.4 Gy, ΔDrcA=-0.3 Gy) and right breast (ΔDrcM=-1.2 Gy, ΔDrcA=-1.3 Gy) maximum dose. Re-optimized dose differences were considered equivalent to recalculated ones for both lungs and LAD, while they were significantly smaller for heart (ΔDroM=-0.2 Gy, ΔDroA=-0.2 Gy) and right breast (ΔDroM =-0.3 Gy, ΔDroA=-0.9 Gy) maximum dose. Twenty-one correlations were found for ΔDrcM,A (M=8,A=13) that reduced to four for ΔDroM,A (M=3,A=1).

CONCLUSIONS

The sensitivity of auto-planning to contouring variation was found not relevant when compared to manual planning, regardless of the method used to calculate the dose differences. Nonetheless, the method employed to define the dose differences strongly affected the correlation analysis resulting highly reduced when dose was reoptimized, regardless of the planning approach.

The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review

There have been many applications and influences of Artificial intelligence (AI) in many sectors and its professionals, that of radiotherapy and the medical physicist is no different. AI and technological advances have necessitated changing roles of medical physicists due to the development of modernized technology with image-guided accessories for the radiotherapy treatment of cancer patients. Given the changing role of medical physicists in ensuring patient safety and optimal care, AI can reshape radiotherapy practice now and in some years to come. Medical physicists' roles in radiotherapy practice have evolved to meet technology for the management of better patient care in the age of modern radiotherapy. This short review provides an insight into the influence of AI on the changing role of medical physicists in each specific chain of the workflow in radiotherapy in which they are involved.

Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning

Background and purpose

Automation in radiotherapy treatment planning aims to improve both the quality and the efficiency of the process. The aim of this study was to report on a clinical implementation of a Deep Learning (DL) auto-planning model for left-sided breast cancer.

Materials and methods

The DL model was developed for left-sided breast simultaneous integrated boost treatments under deep-inspiration breath-hold. Eighty manual dose distributions were revised and used for training. Ten patients were used for model validation. The model was then used to design 17 clinical auto-plans. Manual and auto-plans were scored on a list of clinical goals for both targets and organs-at-risk (OARs). For validation, predicted and mimicked dose (PD and MD, respectively) percent error (PE) was calculated with respect to manual dose. Clinical and validation cohorts were compared in terms of MD only.

Results

Median values of both PD and MD validation plans fulfilled the evaluation criteria. PE was < 1% for targets for both PD and MD. PD was well aligned to manual dose while MD left lung mean dose was significantly less (median:5.1 Gy vs 6.1 Gy). The left-anterior-descending artery maximum dose was found out of requirements (median values:+5.9 Gy and + 2.9 Gy, for PD and MD respectively) in three validation cases, while it was reduced for clinical cases (median:-1.9 Gy). No other clinically significant differences were observed between clinical and validation cohorts.

Conclusion

Small OAR differences observed during the model validation were not found clinically relevant. The clinical implementation outcomes confirmed the robustness of the model.

Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet

The segmentation of the liver is a difficult process due to the changes in shape, border, and density that occur in each section in computed tomography (CT) images. In this study, the Adding Inception Module-Unet (AIM-Unet) model, which is a hybridization of convolutional neural networks-based Unet and Inception models, is proposed for computer-assisted automatic segmentation of the liver and liver tumors from CT scans of the abdomen. Experimental studies were carried out on four different liver CT image datasets, one of which was prepared for this study and three of which were open (CHAOS, LIST, and 3DIRCADb). The results obtained using the proposed method and the segmentation results marked by the specialist were compared with the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and accuracy (ACC) measurement parameters. In this study, we obtained the best DSC, JSC, and ACC liver segmentation performance metrics on the CHAOS dataset as 97.86%, 96.10%, and 99.75%, respectively, of the AIM-Unet model we propose, which is trained separately on three datasets (LiST, CHAOS, and our dataset) containing liver images. Additionally, 75.6% and 65.5% of the DSC tumor segmentation metrics were calculated on the proposed model LiST and 3DIRCADb datasets, respectively. In addition, the segmentation success results on the datasets with the AIM-Unet model were compared with the previous studies. With these results, it has been seen that the method proposed in this study can be used as an auxiliary tool in the decision-making processes of physicians for liver segmentation and detection of liver tumors. This study is useful for medical images, and the developed model can be easily developed for applications in different organs and other medical fields.

Radiation therapist perceptions on how artificial intelligence may affect their role and practice

INTRODUCTION

The use of artificial intelligence (AI) has increased in medical radiation science, with advanced computing and modelling. Considering radiation therapists (RTs) perceptions of how this may affect their role is imperative, as this will contribute to increasing the efficiency of implementation and improve service delivery.

METHODS

A peer-reviewed anonymous survey was developed and completed by 105 RTs between April and June 2021. The online survey was distributed via the Medical Radiation Practice Board of Australia and the Australian Society of Medical Imaging and Radiation Therapy newsletter as well as professional networks. The survey gained perceptions of the impact of AI on radiation therapy practice and RTs roles within Australia, and data were analysed using quantitative data analysis and thematic analysis.

RESULTS

Automation is used throughout radiation therapy practice, with 68% of RTs being optimistic about this. The majority (63%) had little to no knowledge of working with AI and 96% would like to learn more including the underpinnings of AI and its safe and ethical use. Many (66%) perceived AI would affect their role, including increasing their skillset and reducing mundane tasks, whereas others (23%) perceived it would reduce job satisfaction by increasing repetition and limiting their problem-solving ability. AI was perceived to impact the patient positively (67%), increasing efficiency and accuracy of radiotherapy treatments; however, it could depersonalise patient care.

CONCLUSION

RTs perceive embracing AI in radiotherapy has the potential to advance the profession and improve the service to patients. If AI is implemented with sufficient training for greater understanding, and management uses these benefits to improve patient care, rather than replace RTs roles, then overall any negatives will be outweighed by the benefits.

Efficient dose-volume histogram-based pretreatment patient-specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy

BACKGROUND

Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient-specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly.

PURPOSE

To develop a DVH-based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH-based PSQA.

METHODS

A DL model with a three-dimensional squeeze-and-excitation residual blocks incorporated into a modified U-net was developed to predict the measured PSQA DVHs of 208 head-and-neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training.

RESULTS

The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for D_mean of PTV6900 (p = 0.001), D₅₀ of PTV6000 (p = 0.014), D₂ of PTV5400 (p = 0.009), D₅₀ of left parotid (p = 0.015), and D_max of left inner ear (p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases.

CONCLUSIONS

The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH-based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

PURPOSE

Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus.

METHODS

A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes.

RESULTS

A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness.

CONCLUSION

Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

Assessment of efficacy in automated plan generation for Varian Ethos intelligent optimization engine

Varian Ethos, a new treatment platform, is capable of automatically generating treatment plans for initial planning and for online, adaptive planning, using an intelligent optimization engine (IOE). The primary purpose of this study is to assess the efficacy of Varian Ethos IOE for auto‐planning and intercompare different treatment modalities within the Ethos treatment planning system (TPS). A total of 36 retrospective prostate and proximal seminal vesicles cases were selected for this study. The prescription dose was 50.4 Gy in 28 fractions to the proximal seminal vesicles, with a simultaneous integrated boost of 70 Gy to the prostate gland. Based on RT intent, three treatment plans were auto‐generated in the Ethos TPS and were exported to the Eclipse TPS for intercomparison with the Eclipse treatment plan. When normalized for the same planning target volume (PTV) coverage, Ethos plans D_max% were 108.1 ± 1.2%, 108.4 ± 1.6%, and 109.6 ± 2.0%, for the 9‐field IMRT, 12‐field IMRT, and 2‐full arc VMAT plans, respectively. This compared well with Eclipse plan D_max% values, which was 108.8 ± 1.4%. OAR indices were also evaluated for Ethos plans using Radiation Therapy Oncology Group report 0415 as a guide and were found to be comparable to each other and the Eclipse plans. While all Ethos plans were comparable, we found that, in general, the Ethos 12‐field IMRT plans met most of the dosimetric goals for treatment. Also, Ethos IOE consistently generated dosimetrically hotter VMAT plans versus IMRT plans. On average, Ethos TPS took 13 min to generate 2‐full arc VMAT plans, compared to 5 min for 12‐field IMRT plans. Varian Ethos TPS can generate multiple treatment plans in an efficient time frame and the quality of the plans could be deemed clinically acceptable when compared to manually generated treatment plans.

Improving the Quality of Care in Radiation Oncology using Artificial Intelligence

Radiation therapy is a complex process involving multiple professionals and steps from simulation to treatment planning to delivery, and these procedures are prone to error. Additionally, the imaging and treatment delivery equipment in radiotherapy is highly complex and interconnected and represents another risk point in the quality of care. Numerous quality assurance tasks are carried out to ensure quality and to detect and prevent potential errors in the process of care. Recent developments in artificial intelligence provide potential tools to the radiation oncology community to improve the efficiency and performance of quality assurance efforts. Targets for artificial intelligence enhancement include the quality assurance of treatment plans, target and tissue structure delineation used in the plans, delivery of the plans and the radiotherapy delivery equipment itself. Here we review recent developments of artificial intelligence applications that aim to improve quality assurance processes in radiation therapy and discuss some of the challenges and limitations that require further development work to realise the potential of artificial intelligence for quality assurance.

Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group

INTRODUCTION

Artificial intelligence (AI) has started to be increasingly adopted in medical imaging and radiotherapy clinical practice, however research, education and partnerships have not really caught up yet to facilitate a safe and effective transition. The aim of the document is to provide baseline guidance for radiographers working in the field of AI in education, research, clinical practice and stakeholder partnerships. The guideline is intended for use by the multi-professional clinical imaging and radiotherapy teams, including all staff, volunteers, students and learners.

METHODS

The format mirrored similar publications from other SCoR working groups in the past. The recommendations have been subject to a rapid period of peer, professional and patient assessment and review. Feedback was sought from a range of SoR members and advisory groups, as well as from the SoR director of professional policy, as well as from external experts. Amendments were then made in line with feedback received and a final consensus was reached.

RESULTS

AI is an innovative tool radiographers will need to engage with to ensure a safe and efficient clinical service in imaging and radiotherapy. Educational provisions will need to be proportionately adjusted by Higher Education Institutions (HEIs) to offer the necessary knowledge, skills and competences for diagnostic and therapeutic radiographers, to enable them to navigate a future where AI will be central to patient diagnosis and treatment pathways. Radiography-led research in AI should address key clinical challenges and enable radiographers co-design, implement and validate AI solutions. Partnerships are key in ensuring the contribution of radiographers is integrated into healthcare AI ecosystems for the benefit of the patients and service users.

CONCLUSION

Radiography is starting to work towards a future with AI-enabled healthcare. This guidance offers some recommendations for different areas of radiography practice. There is a need to update our educational curricula, rethink our research priorities, forge new strong clinical-academic-industry partnerships to optimise clinical practice. Specific recommendations in relation to clinical practice, education, research and the forging of partnerships with key stakeholders are discussed, with potential impact on policy and practice in all these domains. These recommendations aim to serve as baseline guidance for UK radiographers.

IMPLICATIONS FOR PRACTICE

This review offers the most up-to-date recommendations for clinical practitioners, researchers, academics and service users of clinical imaging and therapeutic radiography services. Radiography practice, education and research must gradually adjust to AI-enabled healthcare systems to ensure gains of AI technologies are maximised and challenges and risks are minimised. This guidance will need to be updated regularly given the fast-changing pace of AI development and innovation.

Artificial intelligence in radiography: Where are we now and what does the future hold?

OBJECTIVES

This paper will outline the status and basic principles of artificial intelligence (AI) in radiography along with some thoughts and suggestions on what the future might hold. While the authors are not always able to separate the current status from future developments in this field, given the speed of innovation in AI, every effort has been made to give a view to the present with projections to the future.

KEY FINDINGS

AI is increasingly being integrated within radiography and radiographers will increasingly be working with AI based tools in the future. As new AI tools are developed it is essential that robust validation is undertaken in unseen data, supported by more prospective interdisciplinary research. A framework of stronger, more comprehensive approvals are recommended and the involvement of service users, including practitioners, patients and their carers in the design and implementation of AI tools is essential. Clearer accountability and medicolegal frameworks are required in cases of erroneous results from the use of AI-powered software and hardware. Clearer career pathways and role extension provision for healthcare practitioners, including radiographers, are required along with education in this field where AI will be central.

CONCLUSION

With the current growth rate of AI tools it is expected that many of the applications in medical imaging will continue to develop to more accurate, less expensive and more readily available versions moving from the bench to the bedside. The hope is that, alongside efficiency and increased patient throughput, patient centred care and precision medicine will find their way in, so we will not only deliver a faster, safer, seamless clinical service but also one that will have the patients at its heart.

IMPACT FOR PRACTICE

AI is already reaching clinical practice in many forms and its presence will continue to increase over the short and long-term future. Radiographers must learn to work with AI, embracing it and maximising the positive outcomes from this new technology.

Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiation therapy (IMRT) and volumetric-arc radiation therapy (VMAT), patient specific quality assurance (QA) should be performed before treatment. IMRT/VMAT dose measurements in a phantom using various devices have been clinically adopted as standard method for QA. This approach allows the verification of the accuracy of the dose calculation, data transfer, and the delivery system. However, patient-specific QA procedures are expensive and require significant time and effort by the physicists. Over the past 5 years, machine learning (ML) and deep learning (DL) algorithms for predictions of IMRT/VMAT QA outcome have been investigated. Various ML and DL models have shown promising prediction accuracy and a high potential as time-efficient virtual QA tool. In this paper, we review the ML and DL based models that were developed for patient specific IMRT and VMAT QA outcome predictions from algorithmic and clinical applicability perspectives. We focus on comparing the algorithms, the dataset sizes, the input parameters and features, the QA outcome prediction approaches, the validation, the performance, the clinical applicability, and the potential clinical impact. In addition, we discuss the present challenges as well as the future directions in the implementation of these models. To the best of our knowledge, this is the first review on the application of ML and DL based models in IMRT/VMAT QA predictions.

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Purpose

The recently published AAPM TG‐275 and the public review version of TG‐315 list new recommendations for comprehensive and minimum physics initial chart checks, respectively. This article addresses the potential development and benefit of initial chart check automation when these recommendations are implemented for clinical photon/electron EBRT.

Methods

Eight board‐certified physicists with 2–20 years of clinical experience performed initial chart checks using checklists from TG‐275 and TG‐315. Manual check times were estimated for three types of plans (IMRT/VMAT, 3D, and 2D) and for prostate, whole pelvis, lung, breast, head and neck, and brain cancers. An expert development team of three physicists re‐evaluated the automation feasibility of TG‐275 checklist based on their experience of developing and implementing the in‐house and the commercial automation tools in our institution. Three levels of initial chart check automation were simulated: (1) Auto_UMMS_tool (which consists of in‐house program and commercially available software); (2) Auto_TG275 (with full and partial automation as indicated in TG‐275); and (3) Auto_UMMS_exp (with full and partial automation as determined by our experts’ re‐evaluation).

Results

With no automation of initial chart checks, the ranges of manual check times were 29–56 min (full TG‐315 list) and 102–163 min (full TG‐275 list), which varied significantly with physicists but varied little at different tumor sites. The 69 of 71 checks which were considered as “not fully automated” in TG‐275 were re‐evaluated with more automation feasibility. Compared to no automation, the higher levels of automation yielded a great reduction in both manual check times (by 44%–98%) and potentially residual detectable errors (by 15–85%).

Conclusion

The initial chart check automation greatly improves the practicality and efficiency of implementing the new TG recommendations. Revisiting the TG reports with new technology/practice updates may help develop and utilize more automation clinically.

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

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.

Dosimetric predictors of patient-reported toxicity after prostate stereotactic body radiotherapy: Analysis of full range of the dose-volume histogram using ensemble machine learning

BACKGROUND AND PURPOSE

This study aims to evaluate the associations between dosimetric parameters and patient-reported outcomes, and to identify latent dosimetric parameters that most correlate with acute and subacute patient-reported urinary and rectal toxicity after prostate stereotactic body radiotherapy (SBRT) using machine learning methods.

MATERIALS AND METHODS

Eighty-six patients who underwent prostate SBRT (40 Gy in 5 fractions) were included. Patient-reported health-related quality of life (HRQOL) outcomes were derived from bowel and bladder symptom scores on the Expanded Prostate Cancer Index Composite (EPIC-26) at 3 and 12 months post-SBRT. We utilized ensemble machine learning (ML) to interrogate the entire dose-volume histogram (DVH) to evaluate relationships between dose-volume parameters and HRQOL changes. The latent predictive dosimetric parameters that were most associated with HRQOL changes in urinary and rectal function were thus identified. An external cohort of 26 prostate SBRT patients was acquired to further test the predictive models.

RESULTS

Bladder dose-volume metrics strongly predicted patient-reported urinary irritative and incontinence symptoms (area under the curves [AUCs] of 0.79 and 0.87, respectively) at 12 months. Maximum bladder dose, bladder V102.5%, bladder volume, and conformity indices (V50/VPTV and V100/VPTV) were most predictive of HRQOL changes in both urinary domains. No strong rectal toxicity dosimetric association was identified (AUC = 0.64).

CONCLUSION

We demonstrated the application of advanced ML methods to identify a set of dosimetric variables that most highly correlated with patient-reported urinary HRQOL. DVH quantities identified with these methods may be used to achieve outcome-driven planning objectives to further reduce patient-reported toxicity with prostate SBRT.

The Economic Impact of Artificial Intelligence in Health Care: Systematic Review

Background

Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the health care industry. It is most relevant for the care provider and insurer as well as for the pharmaceutical and medical technology sectors. Although the broad economic impact of digital health solutions in general has been assessed many times in literature and the benefit for patients and society has also been analyzed, the specific economic impact of AI in health care has been addressed only sporadically.

Objective

This study aimed to systematically review and summarize the cost-effectiveness studies dedicated to AI in health care and to assess whether they meet the established quality criteria.

Methods

In a first step, the quality criteria for economic impact studies were defined based on the established and adapted criteria schemes for cost impact assessments. In a second step, a systematic literature review based on qualitative and quantitative inclusion and exclusion criteria was conducted to identify relevant publications for an in-depth analysis of the economic impact assessment. In a final step, the quality of the identified economic impact studies was evaluated based on the defined quality criteria for cost-effectiveness studies.

Results

Very few publications have thoroughly addressed the economic impact assessment, and the economic assessment quality of the reviewed publications on AI shows severe methodological deficits. Only 6 out of 66 publications could be included in the second step of the analysis based on the inclusion criteria. Out of these 6 studies, none comprised a methodologically complete cost impact analysis. There are two areas for improvement in future studies. First, the initial investment and operational costs for the AI infrastructure and service need to be included. Second, alternatives to achieve similar impact must be evaluated to provide a comprehensive comparison.

Conclusions

This systematic literature analysis proved that the existing impact assessments show methodological deficits and that upcoming evaluations require more comprehensive economic analyses to enable economic decisions for or against implementing AI technology in health care.

Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology

Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased significantly with the introduction of electronic health records and participation in payer-mandated quality initiatives. Artificial intelligence (AI) has the potential to reduce the burden of data entry associated with clinical documentation, provide clinical decision support, improve quality and value, and integrate patient data from multiple sources. The authors discuss key elements of an AI-enhanced clinic and review some emerging technologies in the industry. Challenges regarding data privacy, regulation, and medicolegal liabilities must be addressed for such AI technologies to be successful.