The use of artificial intelligence in healthcare has received a lot of attention lately, and there is no indication that this trend will ever slow down. With everything from mobile coaching apps to medication research falling under the domain of what can be accomplished with machine learning, AI in healthcare has enormous and far-reaching potential. However, there are significant connections between the future of healthcare and that of artificial intelligence and machine learning.
Role of AI in Healthcare
AI is being used in radiology and chronic diseases like cancer to develop accurate and effective inventions that will assist treat patients who are afflicted by these conditions and, ideally, find a cure. Compared to conventional methods of analytics and clinical decision-making, AI offers several benefits. As training data is understood by AI algorithms, the systems become more accurate. This allows humans to get previously unattainable insights into treatment variability, care processes, diagnostics, and patient outcomes.
Most jobs that were previously undertaken by humans can now be completed by the system more quickly and efficiently. This huge benefit has made activities in the health sector easier for all parties involved, particularly for patients, doctors, and hospital administrators.
In a recent analysis, Tractica predicted that by 2025, 22 healthcare AI products would generate $8.6 billion in yearly revenue. By the same deadline, existing usage patterns predict a $34 billion global revenue. Modern machine learning tools are now available that can act, learn, comprehend, and anticipate. This is a step up from earlier AI-driven technologies like surgical assistance robots and genetic code linkage.
Drug Creation by Artificial Intelligence:
To increase the rate at which new drug candidates are recognized while lowering the expenses connected with early key discovery techniques are required.
Exscientia is a well-known AI driven drug discovery company that focuses on the original AI’s capabilities while also having a working understanding of how to find novel therapeutic targets. 2020 saw the announcement from Sumitomo Dainippon Pharma and Exscientia that they had worked together to develop the novel medicine candidate DSP-1181, which was chosen using Artificial intelligence. In contrast to the normal industry lead time of 4.5years, it was shown that the business was driven by strong cooperative energy and shared ground in research, taking less than 12 months to complete the exploratory research stage.
It is the first company to declare that the first AI-designed immune-oncology drug will enter human trials in 2021. The first new medicine entity discovered by AI t, that has been submitted to treat obsessive-compulsive disorder is the new candidate that was developed in conjunction with deep expertise, knowledge, and experience in chemistry and pharmacology on monoamine GPCR drug discovery. Benevolent is another pharma company using AI algorithms to conduct drug discovery and clinical trials.
Applications of Artificial Intelligence in Healthcare:
Clinical Decision Making: The advantages of AI in medicine are well known. Healthcare Information and Management Systems Society Inc. (HIMSS) claims that the use of artificial intelligence (AI) in healthcare has changed clinical decision making by giving decision makers access to crucial real-time data that can be used to diagnose patients, plan treatments, and manage population health. Solutions exist that can make use of insights from genetic, biomarker, and phenotype datasets, as well as solutions with a focus on ophthalmology, pathology diagnosis, and radiology.
Unique Assistance in surgery: Artificial Intelligence development has taken a huge leap in robotic applications. The same is the case for machine learning implementation in surgery. There are dedicated AI Surgical Systems that can execute the tiniest movements with 100% accuracy. This means we can do complex operations efficiently with reduced risks of side effects, blood loss, or pain. Likewise, post-surgery recovery is faster and easier.
Enhanced human capabilities and support for mental health: Now, robots can help patients in addition to medical professionals. For instance, paraplegic individuals can regain their mobility with little to no assistance from caregivers thanks to exoskeleton robots. Like artificial intelligence-powered prosthetic limbs, smart prostheses are equipped with sensors that operate more like natural limbs.
Service robots developed through the application of machine learning can manage everyday duties and keep patient’s company. There are specialized conversational and companion robots that do necessary tests and checks, like monitoring blood pressure, sugar levels, managing temperature, and even administering medication. Due to their built-in analytical abilities, robots have been created to assist depressed individuals. These abilities enable them to assess the mod of the patients and improve their mood.
Challenges faced by Artificial Intelligence in Healthcare:
- Trust ability and Authenticity: - Robots can’t treat patients the way humans can because there’s always a trust building exercise between a doctor and a patient, an algorithm can never understand patients’ emotions while taking life-altering decisions. We don’t even trust machines sometimes in situations where they can perform better than humans. Hence, an algorithm- based robot can never replace humans in the field of healthcare.
- Data Privacy: - A healthcare database is the most sensitive information about an individual which a person can give to another. Several cases have been brought because of data sharing between major health institutions and AI companies, raising privacy concerns among certain individuals who may feel that this collecting may breach their privacy. AI may violate privacy in yet another way: it is capable of foreseeing patient-specific information that was never provided to the algorithm.
- Injuries to patients: - The most obvious risk is that AI system will occasionally be incorrect, which could lead to patient damage or other issues with healthcare. The patient can suffer harm if an AI system gives the wrong medication recommendation, misses a tumor on a radiological exam, or chooses one patient over another for a hospital room based on erroneous predictions about which patient would benefit most.
Healthcare systems might benefit from AI, there is no doubt about it. Automating time-consuming processes can free up clinician schedules so that they can interact with patients more. Enhancing data accessibility helps medical practitioners take the appropriate precautions to avoid sickness. AI is being used to cut down on administrative errors and conserve important resources. As more SMEs get involved in AI development, the technology becomes more useful and informed. Limitations and difficulties are still being faced and overcame as AI is used more and more in the healthcare industry. AI still needs some human oversight, may ignore social factors, has information gaps about the population, and is vulnerable to increasingly sophisticated cyberattacks. Despite various difficulties and restrictions, artificial intelligence (AI) holds tremendous promise for the medical industry.
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