Abstract
Information technologies, specifically intelligent verdict support systems, are becoming a vital tool in healthcare for improving diagnosis, recommending and optimizing therapies, and directing physicians’ policy making decisions. These arrangements integrate machine learning, big data and artificial intelligence that can rapidly investigate large swaths of exceedingly focused medical data to bid commendations. The latter is particularly true of deep learning methods, NLP, and cloud computing integrated into IDSS solutions in recent years. IDSS can be applied in fields like outbreak diagnosis, patient risk profiling, robotic surgeries, and even automated interpretation of radiology reports. In addition, bringing clinical decision support in EHRs has helped minimize human errors and contributed to the improvement of the operations being undertaken. However, there are challenges with implementing IDSS in healthcare, as mentioned below. Information sharing is the greatest challenge because many medical facilities implement disparate data exchange systems. This is credited to issues relating to ethics and law, including patient privacy, protection of data, and even prejudice in algorithms used to implement the model. There are problems associated with accountability since clinicians are forced into a scenario where they constantly use artificial intelligence to make decisions. Yet, they still have to rely on their training and expertise. In addition, its integration with the current processes requires a high degree of training and tuning to become utilized successfully. There are several ways to overcome these challenges, but current research is mainly related to federated learning, which enables machine learning when patient data stays local. Also, explainable AI or XAI is becoming increasingly important as it helps to make the policy making procedure clear so that healthcare workers can comprehend and evaluate the result. These regulatory authorities are also working on guidelines that would set limits and rules for the use of IDSS while maintaining the spirit of innovation needed in the healthcare field while protecting patients’ health. In this context, IDSS will be of growing importance to individualized treatment, boosting efficiency in clinical practice and developing prevention strategies. Bridging the existing gaps with the innovation in multidisciplinary approaches and technology will act as another milestone towards enhancing intelligent decision support in health care.