Through computer science, in reference to human intellectual ability, Artificial Intelligence (AI), also called Machine Intelligence is intelligence exhibited by computers. Founded in 1956 as an academic field, Artificial Intelligence has undergone many waves of excitement over the years, accompanied by frustration and loss of funding (known as “AI winter”), accompanied by alternative approaches, progress, and revived investment.

AI research’s typical concerns (or objectives) include inference, representation of information, preparation, reading, processing of natural language, perception, and the ability to move and process information.

AI research’s conventional challenges (or goals) include reasoning, representation of information, scheduling, training, interpretation of natural language, awareness, and the ability to shift and avoid obstacles. General intelligence is one of the lengthy-term priorities of the sector. The strategies include numerical, mathematical, and conventional conceptual AI approaches.

AI in healthcare

Artificial Intelligence’s ultimate research goal is to create technology that enables computers and machines to operate intelligently.

Descriptive AI

Descriptive AI is probably the most frequently used in biomedical innovation and has the most promising quick-term potential. It takes into consideration incidents that have already taken place and uses this information to gain more insight, such as identifying patterns and subtle changes that might otherwise be prevented by healthcare professionals.

Predictive AI

Predictive AI uses detailed data to try to foresee the future. Medical professionals use AI to provide information and recommend behaviours in a proactive manner. AI may play a vital role in predictive healthcare and hospital administration developments.

Prescriptive AI

Prescriptive AI fosters the intent of predictive AI, not only identifying patterns which may not be anticipated by individuals but also recommending potential clinical nuances-based treatment. This ability to create decisions makes prescriptive AI in the near term the most fascinating and contentious use case.

GROWTH OF AI IN HEALTH CARE

For medicine, there are growing magnitudes of Artificial Intelligence. AI can help in the creation of online services that allow physicians and practitioners to access thousands of therapeutic tools within the blink of an eye. In terms of efficiency, AI can assist a physician, offering both quantitative and qualitative statistics based on feedback, enhancing early diagnosis, precision in treatment and estimation of the outcome. AI’s capacity to “learn” from the information offers an opportunity to enhance effectiveness based on input responses. Medical care AI programs also work in real-time, which ensures that the information is constantly updated, increasing reliability and significance. Physicians have nearly unlimited resources to achieve their care capacity with the collection of continuously updated data.

CHALLENGES TO AI IN HEALTH CARE

DATA

Accessibility to detail is the limitation of AI in healthcare implementation. The primary data concerns involved in gaining consent and making the information secure and accurate.

DESIGN

The need for traditional design requirements for proposed AI systems has now been discussed by industry experts. Standards for different models can help to provide frameworks to ensure the AI approach towards privacy, security, performance and accuracy and address issues of ethics as well as trust.

IMPLEMENTATION AND ADOPTION

The introduction and acceptance of AI pose a number of challenges. Some of these include the following:

• Regulatory Authority: — Medical councils’ authority to regulate the clinical aspects, and a data privacy bill regulator to monitor data issues is required for effective solutions.
• Infrastructure: — For instance, cloud computing architecture is mainly focused on outside servers. Delays in spending on indigenous resources lead to better access to technology and research for start-ups outside the nation.
• Investment: — Expenditure, while increasing, is currently restricted in health-related AI and work is poorly funded and examined
• Asymmetries and interpretations of data: — AI-based medical systems are most often faced with the challenge of asymmetric information between the physicians who use the technology and the programmers who designed the program. In fact, the understanding of AI innovations can be a primary cause of how successfully they can be used in care.

Therefore, design criteria are required to encourage the growth of reasonable AI. Key principles to encourage responsible AI include the following:

1. Transparency (user-visible operations)

2. Explainability (it is possible to trace the whole process followed by making a decision)

3. Suitability (comprehensibility)

4. Legitimacy (acceptable results should be there)

5. Auditability (it is simple to calculate efficiency)

6. Dependability (AI systems are operating as intended)

7. Recoverability (if necessary, manual control may be presumed)

TRUST AND POLITICS

Term “confidence”, sums up the greatest obstacle to the medical acceptance of AI. Patients don’t know whether they can trust new software to provide diagnoses, track their condition or interpret scans when nobody can explain in terms of how it works. A new Data Protection and Development Centre should be established to serve as administrator of data, including confidential information, to make it more accessible to entities within a set of standards and criteria so that the use of information is ethical.

TRUST AND DATA PROTECTION

Data protection standards allow entities (device and technology suppliers) to ensure that confidentiality is protected as a standard in any program and that it is configured to comply with the law. This is to prevent refurbishing measures of security into frameworks and code and to make privacy a prerequisite of layout instead of an afterthought.

Measures for physical and technical security should always be a fundamental principle of data privacy is to ensure that safety is sufficient to the quality of the data and the damage that could be incurred by misuse.

CONCLUSION

Artificial Intelligence has a variety of medical applications which can be carried out by analysing through descriptive, predictive and prescriptive artificial intelligence. AI-powered frameworks are followed by some challenges — they require an effective legal structure to regulate confidentiality and authentication while addressing issues of acceptability, medical intervention, transparency, and clarification. The steps needed to create a thriving health care environment for AI are-

• Strong open data policy
• Rigorous confidentiality regulation
• Deploying labour forces with the relevant skills to embrace AI
• Preparation for the improvements that AI will bring and a regulatory regime that maintains accountability and transparency but does not impede the advancement

Aarsh, Co- Founder & COO, Gravitas AI

www.gravitas.ai

Data science has proven beneficial for various segments of the industries. Healthcare is one of the foremost sectors, which have benefited from big data analytics. In the contemporary healthcare industry across the globe, the demand for data scientists is escalating.

Data science can effectively strengthen this particular industry in several ways. From preventing epidemics to reducing costs and curing diseases, data science has extensively reshaped the modern healthcare industry. Data science, when applied to this particular segment, uses specific data of a population to generate valuable insights for decision making.

Presently, the healthcare industry generates around 30% of the warehouse datain the world. Leaders in the healthcare domain have realized the potential of data science. Integrating data science in healthcare can result in saving around $300 billion annually. Technology has generated the opportunity to leverage the healthcare industry. Business leaders have successfully implemented some of these technologies in the domain of healthcare.

What makes data science important in the healthcare industry?

In the healthcare industry, data analytics has been one of the most important revolutions in the last decade. Adoption of data analytics can pacify healthcare to a significant extent. Here are a few instances that show how data science is changing the healthcare segment.

• The hospitals and healthcare organisations can analyse structured as well as unstructured data using various digital tools like EHRs.
• Based on the available data, medical practitioners can make valuable predictions and inferences using analytics tools and machine learning.
• In developed countries, analytics tools have already been used to enhance clinical outcomes and increase their efficiency.
• Integrating cloud computing infrastructure, large volumes of data can be processed in real time. The data scientists analyse the same to generate valuable insights needed for decision making.
• Presently, leadership in healthcare organisations has become technology-oriented. Hospital authorities are ready to embrace new tools and they are adopting new technologies.

Embracing data science: How is the healthcare industry benefiting from technology?

Here are some areas in the healthcare industry, where data science has already been implemented.

• Medical image analysis

In medical imaging, the healthcare industry has derived great benefits, applying data science. Popular techniques of imaging include computed tomography, X-ray, magnetic resonance imaging (MRI) and mammography, among others. In order to deal with the differences in resolution, modality and image dimensions, various methods are used.

Other techniques are being used to extract information from images and enhance the quality of data. This results in obtaining interpretations that are more accurate. The diagnostic accuracy can be enhanced by integrating deep-learning algorithms, learning from examples to suggest better solutions for treatments. Popular techniques for image-processing focus on denoising, segmentation and enhancement.

• Electronic health records (EHR)

HER is probably one of the most extensive applications of data science in the healthcare sector. This technology allows each patient to have a digital record. This includes medical history, demographics, allergies, results of laboratory tests and so on. Secure information systems are used to share these records.

Providers from both the private and public sector have access to these. A single modifiable file comes with each record. This enables the doctors to implement changes in course of time. This eliminates the possibilities of data replication and paperwork. EHRs have the ability to trigger remainders, in case a patient needs a lab test. They are also used in tracking prescriptions, so that it can be verified whether a patient is adhering to the orders of the doctors.

• Real-time alerts

The leading hospitals use Clinical Decision Support (CDS) software to scrutinise medical data in real time. This saves time and helps the doctors make precautionary choices. For instance, if the blood pressure of a patient shows an alarming rise, the system will send a real-time alert to the medical practitioners. They will take a timely action, reaching the patient and taking the necessary measures to lower the blood pressure.

Analytics, therefore, has become a new strategy in treatment processes. The data from patients is collected by wearables and these are sent to the cloud.The hospitals use sophisticated technology to regulate this massive volume of data. Each time a disturbing result shows up in the form of alerts, the doctors take precautionary actions.

• Improving patient engagement

In recent times, people have been more interested in smart devices and wearables. This has unleashed the opportunity for the healthcare industry to enhance patient engagement significantly. The smart devices can keep a track of their heart rates, number of steps and sleeping habits.

On analysing this information, the doctors can gain important insights about their health conditions. Integrated with other trackable information, this data can be used in identifying possible health risks. For instance, an increased heart rate or chronic insomnia can indicate the possibility of heart disease in future. Therefore, data analytics tools can help people enjoy a healthy lifestyle, where they can monitor their activities on their own.

• Reducing healthcare expenses

Doctors are able to make informed decisions, when they use analytics tools for data processing. As a result, they can help the patients save costs. For instance, when data analytic techniqueswere used to optimise the process of knee replacement, the healthcare provider was able to make an annual saving of 1.2 million dollars.

The organisation can benefit immensely from techniques based on data science. It is for this reason that most of the reputed hospitals across the world are hiring seasoned data scientists.In the process, preventive analytics have turned out to be one of the core areas of data science. In the coming years, technologies are expected to grow smarter. Integrating machine learning and data science in the healthcare industry can eventually reduce treatment costs.

Wrap-Up

Other areas where data science has strengthened the healthcare industry is the creation of drugs, and virtual assistance for customer support and patients. Presently, machine learning algorithms are being used to process natural language and generate accurate information.

This can be used to map the patient’s condition accurately and deliver a personalised experience. Diagnostic accuracy has been enhanced manifold using data science. The coming years are likely to witness further sophistication of data science technologies.

 

Mukesh Kumar Sinha, Co- Founder & COO, Gravitas AI

www.gravitas.ai