Artificial intelligence (AI) in healthcare refers to the use of algorithms trained with machine learning (ML) in the three macro-categories that make up the healthcare market: the application of healthcare (life sciences), the delivery of healthcare (healthcare providers), and the use of healthcare (payers).
Recent advances in machine learning for algorithm training have expanded the use cases for AI in healthcare, far surpassing the early reactive AI. Advances in machine learning are critical to the transformation and modernization of healthcare .
In order to analyze and implement solutions based on medical data, it is necessary to make such data accessible and usable. Only in this way can we obtain more widespread, effective and efficient applications of health services.
Remote Monitoring and Telemedicine
The field of telemedicine has been greatly expanded by the adoption of AI. This technology allows doctors to monitor and treat patients remotely, reducing the need for hospital visits and making healthcare more accessible, especially in remote or underserved areas.
An integrated digital system for home monitoring of patients
The first “major challenge” concerns the creation of an integrated digital system for patient home monitoring. Traditional health assessments, which rely on clinic visits, direct contact with doctors and nurses, and annual laboratory tests, are cumbersome and expensive, with delayed analytical results and potentially late diagnoses. Consequently, this traditional approach does not allow for continuous and longitudinal patient assessment and is limited to applications where infrequent testing is sufficient, thus failing to meet the growing health needs of the rapidly growing population.
Here are some ways AI in healthcare is proving to be a powerful asset for patients, providers, and payers:
Faster Diagnosis
AI algorithms and real-time predictive analytics provide insights into faster diagnoses. AI then helps patients get treatment faster, which increases the likelihood of recovery and allows fewer resources to be used to resolve the problem. For example, a leading healthcare website in the United States, used perfect solutions to build a real-time predictive analytics system that could more quickly and accurately detect signs of sepsis , a life-threatening condition.
Claims Management
The paperwork involved in claims and payments can take up hours of administrative time. In addition, processing claims manually increases the risk of errors, which is detrimental to both patients and providers. AI automates submissions and provides data-driven suggestions for claims management, helping you speed up claims processing and provide a seamless experience for your employees and customers.
Fraud, Waste, and Abuse
Robotic Process Automation (RPA) can examine documents with a speed and accuracy far superior to manual intervention. These algorithms are therefore useful for detecting fraudulent activity or waste; and they improve over time, becoming more effective at detecting problems.
Expanding access to healthcare offerings
AI-assisted diagnostic systems increase the number of patients receiving services. For example, AI-assisted radiology or biomedical imaging allows more professionals to interpret ultrasounds and helps reduce bottlenecks caused by the lack of specialists and increase the number of patients undergoing these tests.
Drug Development
Developing new drugs requires determining doses and delivery methods. Computational AI tools can improve or even replace trial-and-error approaches and provide faster, more efficient models to monitor the entire process. This can speed up the development of new drugs and reduce costs for the pharmaceutical company and end customers.
AI in Diagnosis, Treatment and Personalization of Care
One of the most significant uses of AI is in the early diagnosis and treatment of diseases . Advanced algorithms are able to analyze large volumes of data, such as diagnostic images, to detect anomalies that could escape the human eye. In oncology, for example, AI is used to analyze radiological images to identify tumors, sometimes surpassing the precision of specialists.
To achieve this result, it is necessary to create an integrated environment that, starting from the development of a digital record, allows for constant analysis of patient data using systems based on artificial intelligence.
Adoption Of Generative Ai In Healthcare
Specifically, in the healthcare sector, generative AI is accelerating drug discovery, improving the planning and execution of clinical trials, and increasingly leading to the personalization of medicine.
Furthermore, as also reported in the recent White Paper “Patient First Health with Generative AI: Reshaping the Care Experience” by the World Economic Forum, it is proving to be a valid tool for improving patient involvement in the care path and for changing the way in which the latter can access health information, receive assistance and manage their health conditions.
An Ever Smarter World of Health
Looking to the future, it is clear that artificial intelligence will play an increasingly important role in the healthcare sector. The potential of this technology to improve the diagnosis and treatment of diseases, optimize the management of healthcare resources, and expand access to care is enormous.
However, with this potential comes significant responsibility. We must ensure that the use of AI in healthcare respects the principles of data privacy, transparency, fairness, and accountability. Additionally, we need to invest in training healthcare professionals and research to overcome the current limitations of AI.
