THE ROLE OF ARTIFICIAL INTELLIGENCE IN CLINICAL PRACTICE

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KEY MESSAGES
Artificial intelligence (AI) has progressively been developed and introduced into virtually all areas of medicine, from primary care to rare diseases, emergency medicine, biomedical research and public health. Many management aspects related to health administration (e.g. increased efficiency, quality control, fraud reduction) and policy are also expected to benefit from new AI-mediated tools.1
The potential for the application of AI in the clinical setting is enormous and ranges from the automation of diagnostic processes to therapeutic decision making and clinical research.1 The data necessary for diagnosis and treatment comes from many sources, including:1
AI includes various techniques such as machine learning (ML), deep learning (DL), and natural language processing (NLP).2

AI assistance in diagnostics

  • AI can revolutionize different aspects of health care, including diagnosis.2
  • ML is an area of AI that uses data as an input resource in which the accuracy is highly dependent on the quantity as well as the quality of the input data that can combat some of the challenges and complexity of diagnosis.2
  • AI tools can improve accuracy, reduce costs, and save time compared to traditional diagnostic methods.2

AI assistance in treatment

Personalized treatment, also known as precision medicine or personalized medicine, is an approach that tailors medical care to individual patients based on their unique characteristics, such as genetics, environment, lifestyle, and biomarkers.2
  • The potential applications of AI in assisting clinicians with treatment decisions, particularly in predicting therapy response, have gained recognition.2
  • AI plays a crucial role in dose optimization and adverse drug event prediction, offering significant benefits in enhancing patient safety and improving treatment outcomes.2

Predictive analytics & risk assessment

Population health management increasingly uses predictive analytics to identify and guide health initiatives.2
  • ML algorithms and other technologies are used to analyze data and develop predictive models to improve patient outcomes and reduce costs.2
  • Predicting hospital readmissions is another area where predictive analytics can be applied. By analyzing patient demographics, medical history, and social health factors, predictive models can identify patients at higher risk of hospital readmissions and target interventions to prevent readmissions (Fig. 1).2

AI powered patient care

Virtual health assistants have the potential to significantly improve the quality, efficiency, and cost of healthcare delivery while also increasing patient engagement and providing a better experience for them.2

Chatbots & language models

  • The application of greatest potential and concern is the use of chatbots to make diagnoses or recommend treatment. A user without clinical experience could have trouble differentiating fact from fiction.3
  • The new generation of chatbots can help with the medical documentation problem and answer key questions that could help in the differential diagnosis. However, it is difficult to know whether the answers provided are grounded in appropriate fact.3

AI Limitations

it is important to address limitations such as bias and lack of personalization to ensure equitable and effective use of AI.2
REFERENCES
  1. Think Tank. European Parliament, 06/2022. Artificial intelligence in healthcare: Applications, risks, and ethical and societal impacts. Accessed September 2024.
  2. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z.
  3. Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023 Mar 30;388(13):1201-1208. doi: 10.1056/NEJMra2302038.and improving treatment outcomes.2