▲圖片來源：insideBIGDATA

In this special guest feature, Amir Atai, Ph.D. is Co-Founder and CEO of Sway AI, examines how AI is changing healthcare by improving the efficiency and quality of care on many fronts, starting with administration. Sway AI is a developer of no-code AI technologies and services. Amir is an expert in complex modeling techniques, math, and statistics, where he has developed cutting-edge modeling tools.

Artificial intelligence (AI) is driving innovation everywhere, including in healthcare. Medical professionals benefit from the ability to apply machine learning (ML) to everything from processing electronic health records (EHRs) to facilitating diagnosis and treatment. While some may feel that AI removes the human element from healthcare, the truth is that automation and ML are making nurses and doctors more efficient, giving them deeper insights, and freeing time to deliver better and more personalized patient care.

AI continues to offer new benefits for healthcare. For applications such as automating paperwork, AI-powered automation relieves the burden of repetitive tasks and reduces human errors. AI is also being used to make surgeons more efficient and medical procedures, and patients also receive benefits through advancements such as personalized treatments and more streamlined visits. Applying AI-powered learning algorithms is improving diagnostic imaging and identifying infection patterns.

The adoption of AI-driven solutions is limited by the cost of software development and the complexity of supporting AI applications. Furthermore, medical experts often complain about the lack of explainability in AI techniques and the lack of sensitivity analysis on the final solution. Fortunately, no-code artificial intelligence solutions are putting AI control in the hands of medical practitioners.

How AI Is Changing Healthcare

AI is improving the efficiency and quality of care on many fronts, starting with administration.

Regular nurses in the U.S. spend an average of 25% of their time on regulatory and administrative tasks, many of which can be automated. Electronic health records (EHRs) and automated regulatory systems have reduced the administrative workload for nursing staff, giving them more time for patient care. Automating repetitive tasks, such as completing intake forms, note-taking, and scheduling follow-up appointments, also eliminates data entry errors and streamlines administrative tasks. While AI has made administrative tasks more efficient, nurses still need to take charge of patient care. Providing self-service tools such as no-code AI processes allows nurses to design their own processes customized to specific administrative procedures.

Artificial Intelligence is also being used to streamline medical treatment. A virtual nurse can ask about symptoms and provide information about health problems and medications, a helpful tool when patients can’t get an appointment to see a doctor. Furthermore, personalized treatment through biosensors and AI can be effectively achieved by leveraging machine learning techniques and data obtained through specialized biosensors. AI also is being used for health monitoring and to promote patient wellness.

AI and ML are needed to process the increasing amount of machine data. Healthcare currently generates about 30% of the world’s data volume, and the compound annual growth rate (CAGR) of healthcare data is expected to reach 36% by 2025. AI can apply deep learning approaches to assess and normalize large unstructured data sets for analytics and clinical applications.

AI also promotes greater accuracy for medical diagnosis. For example, using AI techniques computers can be used to scan MRIs to detect tumors with incredible accuracy. Smart devices are also being deployed in the ICU and other clinical settings to monitor patients and identify issues such as cardiac arrhythmias occurrence, treatment complications, or sepsis infection. Additional applications that are playing a significant role in enhancing doctors’ ability to save lives are automated anomaly detection which can provide real-time colon polyp detection during colonoscopies and the detection of cancer in mammography through using advanced imaging technologies and an AI engine which detects subtle cancer cells that are often obscured by dense breast tissues.

Drug discovery is another area where AI is having a substantial impact. For example, pharmaceutical companies are using AI to design new molecules to treat cancer and other illnesses.

Challenges to Using AI in Healthcare

While AI continues to find new applications in healthcare, there are still challenges to its adoption:

Data governance

Privacy regulations such as HIPAA are designed to protect patient data but can also impede automation adoption. For AI to continue to find new applications in treatment and EHR management, privacy laws need to be considered.

Optimizing electronic records

Data tends to be scattered across multiple databases, each with its own data structure. Fragmented information needs to be centralized and normalized to support applications such as patient treatment.

Lack of data scientists

There is an ongoing shortage of AI experts. Data scientists are in high demand, and the U.S. Bureau of Labor Statistics estimates a 33% increase in demand through 2030.

To address these challenges and make the most of AI technology, healthcare professionals are building their own AI-driven solutions using no-code platforms. Putting the subject-matter experts in charge of application design makes it easier and faster to create AI-driven processes that address administrative and patient needs and are regulatory compliant.

The Value of No-code AI

Many situations are ideally suited for no-code AI:

AI is ideal for repetitive tasks, such as data entry, maintaining patient records, or completing forms. AI is increasingly being used to capture and process data, including data classification, data extraction, and data validation, to match information against other data sources.

AI is useful for diagnostics since it can integrate and analyze data from multiple sources. For example, AI can match symptoms to likely causes, allowing doctors to draw from diagnostic data beyond their expertise and reducing diagnostic errors. AI is ideal for performing “what if” scenarios to help pinpoint the cause of disease.

Machine learning makes it possible to use learning algorithms to improve results. Interacting with training data provides additional insights and improves outcomes. Machine learning algorithms can be helpful in diagnosis and treatment and lead to better patient outlines. AI also can reduce costs by freeing time for nurses and doctors and reducing hospital-acquired condition penalties.

As AI is increasingly applied in healthcare, you also can expect to see more low-code/no-code tools emerge to help healthcare professionals design their own solutions. Putting the experts in charge of building their own applications is the best way to develop practical AI applications without relying on developers.

It’s clear that AI is changing our approach to healthcare. Using AI and ML to automate mundane tasks and add new diagnostic and treatment solutions will make doctors and nurses more efficient, freeing time to do what they do best – treating patients and making their life better.

轉貼自： insideBIGDATA

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