Artificial Intelligence Application in Healthcare

• It is generally believed that Al tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff.
• Al is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring.
• There are different opinions on the most beneficial applications of Al for healthcare purposes. Forbes stated in 2018 that the most critical areas would be administrative workflows, image analysis, robotic surgery, virtual assistants, and clinical decision support.

 

Precision medicine

• Precision medicine provides the possibility of tailoring healthcare interventions to individuals Precision Medicine or groups of patients based on their disease profile, diagnostic or prognostic information, or their treatment response.
• The tailor-made treatment opportunity will take into consideration the genomic variations as well as contributing factors of medical treatment such as age, gender, geography, race, family history, immune profile, metabolic profile, microbiome, and environmental vulnerability.
• The objective of precision medicine is to use individual biology rather than population biology at all stages of a patient’s medical journey. This means collecting data from individuals such as genetic information, physiological monitoring data, or EMR data and tailoring their treatment based on advanced models. Advantages of precision medicine include reduced healthcare costs, reduction in adverse drug response, and enhance effectivity of drug action.
• Innovation in precision medicine is expected to provide great benefits to patients and change the way health services are delivered and evaluated. There are many types of precision medicine initiatives and overall, they can be divided into three types of clinical areas: complex algorithms, and digital health applications.

 

Complex algorithms

• Machine learning algorithms are used with large data sets such as genetic information, demographic data, or electronic health records to provide a prediction of prognosis and optimal treatment strategy.

 

Digital health applications

• Healthcare apps record and process data added by patients such as food intake, emotional state or activity, and health monitoring data from wearables, mobile sensors, and the like,
• Some of these apps fall under precision medicine and use machine learning algorithms to find trends in the data and make better predictions and give personalized treatment advice.

 

Drug discovery and development

• Drug discovery and development is an immensely long, costly, and complex process that can often take more than 10 years from the identification of molecular targets until a drug product is approved and marketed. Any failure during this process has a large financial impact, and in fact, most drug candidates fail sometime during development and never make it onto the market.
• On top of that are the ever-increasing regulatory obstacles and the difficulties in continuously discovering drug molecules that are substantially better than what is currently marketed. This makes the drug innovation process both challenging and inefficient with a high price tag on any new drug products that make it onto the market.
• There has been a substantial increase in the amount of data available assessing drug compound activity and biomedical data in the past few years. This is due to the increasing automation and the introduction of new experimental techniques including hidden Markov model-based text-to-speech synthesis and parallel synthesis.
• However, mining of large-scale chemistry data is needed to classify potential drug compounds efficiently and machine learning techniques have shown great potential.
• Methods such as support vector machines, neural networks, and random forests have all been used to develop models to aid drug discovery since the 1990s.
• There are various tasks in the drug discovery process where machine learning can be used to streamline the tasks. This includes drug compound property and activity prediction, drug-receptor interactions, and drug reaction prediction.

 

Machine vision for diagnosis and surgery

• Computer vision involves the interpretation of images and videos by machines at or above human-level capabilities including object and scene recognition. Areas, where computer vision is making an important impact, include image-based diagnosis and image-guided surgery.

 

Computer vision for diagnosis and surgery

• Computer vision for diagnosis and surgery Computer vision has mainly been based on statistical signal processing but is now shifting more toward the application of artificial neural networks as the choice for learning methods. Here, DL is used to engineer computer vision algorithms for classifying images of lesions in the skin and other tissues. Video data is estimated to contain 25 times the amount of data from high-resolution diagnostic images such as CT and could thus provide a higher data value based on resolution over time. Video analysis is still premature but has great potential for clinical decision support. As an example, video analysis of a laparoscopic procedure in real-time has resulted in 92.8% accuracy in the identification of all the steps of the procedure.