they must be included while proving a model’s clinical validity. Preliminary testing in ‘silent’ conditions can help ensure the model’s viability in production settings [69]. Even while there is some proof that a model is safe to employ during run-in, it could be difficult to predict how well it would perform in really unusual scenarios. You need to prove clinical validity before you can prove clinical value. This differs from performance validation in that it incorporates the assessment of clinically significant metrics. Achieving good results on widely used endpoints like sensitivity, specificity, or area under the receiver operating characteristic curve may be sufficient for certain diagnostic applications. However, clinical outcomes must be verified at every stage of the treatment pathway for their influence to be felt in the real world. This translates to better quality of life, less healthcare resource use, a higher likelihood of survival, better management of the illness, and decreased risk of adverse effects in cancer. Randomized research is the best way to evaluate these hypotheses. The most effective method would be to randomly assign patients to the AI technique and then directly compare the clinical outcomes. Research in this area has been conducted on occasion. The accuracy of colonoscopy polyp detection rates is one such example [70]. The rate of tumour detection was the primary outcome of this research. More study is required to determine how AI systems might enhance the quality of life or living in the long term, although they were proven to be superior. A second approach to AI clinical trials is to employ randomized therapies once a verified model has classified all patients’ risks. One research that attempted this and succeeded used EHR data to identify radiation patients at risk of ED visits [71]. The next step was to randomly assign high-risk individuals to either routine treatment or additional checkups to ensure their health. Both the number of visits to the emergency department and the length of hospital stays were significantly reduced in the randomized high-risk individuals who received additional visits compared to the low-risk patients who did not get such treatment. Although this research design is not sufficient for clinical benefit evidence, it is perfect for AI-based risk-prediction models—a significant portion of AI models that are currently in development. It usually takes a lot of time and effort to conduct a randomized clinical trial. AI interventions are already challenging, and their unique characteristics further make things worse. With the addition of fresh data, AI models can improve with time. What would the protocol be for including this in a typical randomized trial? There has to be a re-evaluation of the conventional randomized clinical trial if AI is to be demonstrated to have therapeutic utility through randomized trials [70]. To identify issues and enhance the experience, the platform should provide a means for users to provide feedback [71]. Systems must be able to communicate with one another at the point of care, inside and across facilities for operations to operate smoothly [72]. Additionally, each dataset under consideration has its own distinct set of usability issues. There are several challenges associated with the new data streams, such as mobile health data and wearable activity trackers [73]. Ensuring the AI software is straightforward to comprehend is a crucial aspect of producing anything functional. The increasing complexity of data streams makes it more difficult to attribute algorithmic predictions to underlying biological or clinical factors. While this ‘black box’ effect might work in other consumer goods industries, it would be extremely challenging to apply it to healthcare decisions

nurses, PAs, therapists, social workers, and even medical students. Who exactly is the ‘designated user’ whose job it is to make use of and share such data in this scenario? For instance, what if a patient’s CT scan includes an AI-generated cancer diagnosis? What if this prediction is subsequently inputted into another algorithm that suggests surgery as a treatment? Who is liable for decisions taken following the plan is another concern that arises. For the time being, we do not have concrete solutions to these issues, and we expect that they will arise individually, case by case. To assist medical AI developers and cancer care professionals in navigating these complex issues, further funding, research, and direction are required for this clinical orchestration of AI models. Although there are currently very few AI applicationsfor oncologic indications that havebeenauthorizedbytheFDA, there are manymorein the works.As a result,there is a lot of interestin findingways to streamlinethe processfrom development to clinical translation.So, the FDA is now workingon prescribingAI and ML-specific protocols for clinical usage. The most current plan of action takes into account the aforementioned clinical principles and lays the groundwork for adding more details to a framework for the safe clinical translation of AI [80].