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The adoption of AI development in healthcare has amplified and is certainly contributing to improved diagnostic accuracy, enhanced patient outcomes and optimized course of treatment. In today’s increasingly technological world, questions swirl around the potential impact of AI in the healthcare industry, especially regarding the role of healthcare professionals.
Concerning questions, “Can AI replace doctors?” or “Will doctors using AI replace those who are not using it?” have been echoing. To shed light on this fascinating, sometimes controversial topic, this piece focuses on emphasizing the augmentative role of artificial intelligence in medicine, underlining that AI is complementing, rather than replacing doctors, nurses and healthcare providers.
The fundamental solution proliferates with the human-AI collaboration, merging the cognitive strengths of healthcare providers with the analytical capabilities of AI.
AI in custom healthcare app development circumscribes a “rule-based expert system” i.e. it uses prescribed knowledge-based rules to address a challenge accompanied by “Robotic Process Automation” which leverages automation technologies to simulate human worker’s tasks.
Primarily, critical issues like medical errors, burnout among professionals, inefficient data management and increasing costs are among the handful ready for AI-driven innovations. Accompanied by Accenture report, AI for healthcare can save $150 billion annually solely in the US by 2026.
But far beyond the numbers and potential savings is a quest for human longevity, and peace of mind and body through technology. We have seen how AI-powered diagnostic tools can early detect the disease, paving the way to better patient outcomes. Also, how AI automates administrative tasks, enabling medics to focus on what matters the most – Patient’s care.
Here’s an overview of some of the most meaningful transformations expected to be seen in the near future of the healthcare industry.
It goes without saying, that AI assists in diagnosing conditions by interpreting data and giving clear and in-depth insights into what is understood about the patient.
Artificial intelligence has the potential to examine hundreds of X-rays, MRI and CT scans and provide a statistical synopsis of its findings. This will drive more accurate, data-driven diagnosis of many common or not-so-common conditions. Furthermore, this communication is fine-tuned depending on the role of healthcare professionals using it, whether a doctor, nurse or specialist.
Communicating only the insights related to them means less noise between the professionals and the specific piece of information actually needed. The algorithms driving these AIs are based on predictive and statistical models, like Natural Language Processing and Machine Learning depending on “training” from existing data sets that are human-reviewed and annotated.
The World Economic Forum has also estimated that AI and Large Language Models will lead to improved outcomes as it turns easy to extract data from the many disparate and siloed sources that traditionally existed across healthcare.
It’s increasingly used in creating synthetic data that is artificially created resembling real-world information. This is especially beneficial for scenarios with confined training data, like with some rare medical conditions and diseases. It also minimizes the security and data protection measures that medical practitioners must consider when working with real patients’ personal data. Synthetic data can also be leveraged to simulate healthcare situations like epidemics or the emergence of antibiotic-resistant organisms that could cause a worldwide healthcare crisis.
It’s becoming increasingly common for doctors and medical professionals to leverage AI for automating many of the repetitive and routine administrative tasks on a daily basis. According to One Study, doctors spend more than half of their time maintaining Electronic Health Records. AI in healthcare will free up their time, allowing them to focus on taking patient care and continuing their training and learning.
Everything from managing and updating patient records to scheduling appointments, healthcare professionals take care of many time-consuming tasks that can be optimized or even taken wholly by AI.
AI can drive more effective and efficient EHR management by smartly organizing doctor’s notes, medical imaging and test results. Plus, it provides a quick medical synopsis of patients, focusing on their concerning health aspects and generating reports for other practitioners.
In essence, automating many of these manual tasks is likely to reduce errors impacting quality of care and patient outcomes.
The same potential that allows generative AI to draft text and writing can also be utilized to create drugs and vaccines for clinical trials. This means that researchers can pace up the lengthy process of selecting potential candidates.
In the past year, Oxford-based Biotech firm Etcembly created the first immunotherapy drug with the help of Generative AI.
The process promises to accelerate the transition of potentially lifesaving new treatments from lab to patient, eventually leading to improved patient outcomes. This means that doctors and nurses, healthcare researchers and scientists will also use AI tools to allow them to work more efficiently and quickly.
With every form of healthcare AI – it still needs humans for AI training, evaluation of outputs, and reconsidering the impact of AI recommendations.
After all, what artificial intelligence in medicine can’t do is replace the “gut feeling” of doctors and this won’t change in 2024.
The thinking process and clinical reasoning that doctors engage in are so complex along with the sources of information that the human brain considers in patient care are endless to capture with present algorithms. The absolute knowledge an expert depends on for effective clinical trials is so deeply integrated in humans, that approaches to get at these data points often fail.
On top of this, AI algorithms and AI-accessible data can also have flaws. Machine learning can be overly smart, leading to over-diagnosis in some patient’s cases. Natural Language Processing also acts like a Trojan horse in healthcare. Its communication is so convincing that it tricks users into thinking it’s knowledgeable in the same way a medical professional is.
Artificial intelligence in healthcare is taking on roles of “clinical-decision-making support”, meaning doctors are in charge and human intelligence is prioritized, while AI complements this. Considering this AI can be programmed to notify the healthcare provider, of all the variables not considered in the algorithm, to help them discover to what extent can AI recommendations be beneficial in a specific context.
AI can fix many of healthcare’s biggest challenges but it’s still far from reality. One of the demur of making this a reality is data. Though, it’s not a tough nut to crack to invent all the promising technologies and machine learning algorithms without appropriate and well-defined data, it’s daunting to realize the complete potential of AI in healthcare.
Undoubtedly, the healthcare industry needs to digitize medical records, standardize the data infrastructure, and create an iron-clad system to protect confidentiality and manage data consent from patients. Without these significant changes and collaborations in the healthcare industry, it would be onerous to achieve the true potential of AI to help humans.
Artificial intelligence holds immense potential, offering significant benefits and opportunities promising to revolutionize the healthcare space. The rise of medical AI could alter the trajectory of human history and transform the healthcare industry. Technologies like AI, ML NLP, and 3D bio-printing might one day replace damaged organs as easily as machine parts.
The adoption of AI in healthcare app development is accelerating rapidly, with India emerging as a hotbed of innovations for AI-based healthcare services. While AI will enhance physician performance rather than replace human doctors, it will also make healthcare more accessible and affordable for patients. If you are planning to test the water and fully harness the benefits of AI in healthcare, proper regulations and a legal framework are essential.
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