Revolutionizing Medicine: The Cutting-Edge Ways Artificial Intelligence is Changing Healthcare
Unlocking the Potential of AI-Powered Tools and Systems to Improve Patient Outcomes and Healthcare Delivery.
Artificial intelligence in medicine is the use of machine learning algorithms to explore medical data and reveal insights to assist improve health outcomes and patient experiences. Artificial intelligence (AI) is increasingly becoming a vital aspect of modern healthcare as a result of recent developments in computer science and informatics.
Currently, the most common roles for AI in medical contexts are:
1. Clinical Decision Support
Throughout clinical trials, a significant amount of work is spent assigning medical codes to patient outcomes and updating the relevant databases. By offering a speedier and more knowledgeable search for medical codes, AI can aid in hastening this procedure.
Recently, two IBM Watson Health clients found that using AI resulted in a reduction of more than 70% in the amount of time spent on medical code searches.
Example:
IBM Watson for Oncology is a clinical decision support system driven by AI. In order to deliver tailored therapy suggestions for cancer patients, this system analyses patient data, including medical histories and clinical notes, using natural language processing and machine learning algorithms.
2. Imaging Analysis
AI already had a significant impact on medical imaging. According to research, AI powered by artificial neural networks can be equally as proficient as radiologists in identifying breast cancer and other illnesses’ symptoms. AI may assist physicians in spotting early illness indicators as well as making the overwhelming volume of medical pictures they must maintain more manageable by identifying important details of a patient’s history and displaying the relevant photos to them.
Example:
Google’s DeepMind Health, which applies machine learning to analyse retinal scans for the early diagnosis of eye illnesses, and Aidoc, which applies AI to identify and prioritise essential cases in radiology departments, are two examples of AI-powered medical imaging applications.
3. Drug Development
One of the most time-consuming and expensive phases of medication development is frequently drug discovery. The main two ways AI might lower the cost of producing new drugs are through improving medication designs and identifying possible new drug combinations. Many of the big data problems that the life sciences sector is currently facing may be solved with AI.
Example:
Atomwise, which utilises AI to predict how tiny compounds will interact with protein targets, and Insilico Medicine, which uses AI to find medication candidates for certain disorders, are two examples of AI-powered drug discovery tools.
4. Patient Monitoring
Devices for remote patient monitoring are being created with AI, enabling medical professionals to keep tabs on patients’ health outside of conventional clinical settings. These gadgets gather real-time information on a patient’s vital signs, level of activity, and other health parameters. This information may then be evaluated using AI algorithms to provide insights and spot potential health problems.
In general, AI-driven remote patient monitoring has the potential to enhance patient outcomes by enabling earlier intervention, lowering the need for in-person sessions, and offering more individualised treatment regimens.
Example:
With the COVID-19 epidemic, telehealth has gained popularity as a means of ensuring the security of both patients and medical professionals. In especially for patients with COVID-19, machine learning and image processing techniques have been extremely important in telehealth monitoring. AI techniques are capable of remotely monitoring a patient’s vital indicators, including blood pressure, heart rate, respiratory rate, oxygen saturation (SpO2), and cough analysis.
5. Electronic Health Records
Healthcare practitioners store and manage patient health information digitally in Electronic Health Records (EHRs). AI-powered solutions are being employed more often to improve the effectiveness and efficiency of EHR systems.
Data interpretation and analysis is one application of AI to EHRs. Healthcare clinicians may not instantly see patterns and trends that AI algorithms evaluate in massive amounts of data in EHRs. This can aid healthcare professionals in making better informed choices regarding patient care and treatment.
Example:
Artificial intelligence (AI)-enabled solutions may be used to examine EHR data and find individuals who are at high risk of contracting illnesses like diabetes or heart disease.
7. Robotics in Healthcare
The use of robotics in surgery and other medical operations has revolutionised these processes. Medical robots are being created thanks to advances in artificial intelligence (AI), which can help surgeons execute operations with greater precision and fewer difficulties.
AI is being utilised to create medical robots for a number of operations, including telemedicine, rehabilitation, and minimally invasive surgery. These robots may be managed remotely by clinicians or can make decisions on their own, using AI algorithms.
Example:
The da Vinci Surgical System is a robotic medical device that uses AI. With the use of this device, surgeons may carry out minimally invasive procedures with more accuracy and control, which helps patients recover more quickly and with less discomfort. The da Vinci system gives the surgeon a 3D picture of the surgical site and employs AI algorithms to convert the surgeon’s hand movements into exact movements of the surgical tools.
Conclusion
AI is transforming healthcare and has the potential to greatly enhance patient outcomes and service quality.
It is crucial to remember that AI is not intended to replace health care providers, but rather to help them make better judgements.
