Global artificial intelligence in diagnostics market size to exhibit a 32.3% CAGR over 2020-2027

Selbyville, Delaware, Dec. 01, 2020 (GLOBE NEWSWIRE) — As per credible estimations, global artificial intelligence in diagnostics market accounted for USD 288 million in the year 2019 and is projected to register a noteworthy CAGR of 32.3% during 2020-2027.

The emergence of the Covid-19 pandemic has plummeted the global economy, arresting the market growth as well as revenue generation by disturbing the entire supply chain. The holistic study of the report aims at responding all client interrogations and suggest business strategies for stakeholders to adapt to the instabilities in the industry. Moreover, it incorporates a detailed investigation of the prominent industry partakers along with their product offerings, profit stake, and manufacturing capabilities.

The business intelligence report by Market Study Repot LLC states that vast applications of artificial intelligence technologies in the healthcare vertical such as patient monitoring care, personalized medicines, drug development, and medical imaging coupled with rising adoption of machine learning and artificial intelligence technologies in diagnostic procedures for higher accuracy as well as patient safety and affordability are augmenting the growth of global artificial intelligence in diagnostics market size.

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Additionally, implementation of AI technologies in medical diagnostic devices and systems, rising investments towards healthcare AI, strategic alliances among healthcare facilities and AI service providers, and growing awareness towards patient safety and care are stimulating the global artificial intelligence in diagnostics market outlook. However growing security concerns along with high costs associated with the implementation of AI technologies are expected to hinder the industry expansion throughout the analysis timeframe.

Citing an instance, as per the Center for Internet & Society (CIS), digital healthcare companies raised around USD 5.5 billion for implementation of AI technologies in the Indian healthcare vertical in 2017.

Outlining market segmentations:

Based on component type, global artificial intelligence in diagnostics industry is fragmented into services, hardware, and software.

With regards to diagnosis type, the overall market is bifurcated into neurology, chest & lung, radiology, pathology, oncology, cardiology, and others.

Regional outlook

From a regional frame of reference, global artificial intelligence in diagnostics market is divided into Latin America, Europe, North America, Asia-Pacific, and rest of the world. The country wise bifurcation of the market comprises Mexico, Brazil, Australia, Japan, South Korea, India, China, rest of APAC, France, Germany, Spain, United Kingdom, Italy, rest of Europe, Canada, and U.S.

North America accounts for a majority of global artificial intelligence in diagnostics market share, on account of high concentration of vendors along with increasing adoption of healthcare IT solutions in medical diagnosis.

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On the other hand, Asia-Pacific artificial intelligence in diagnostics market is predicted to register the highest CAGR over 2020-2027. Favorable government initiatives towards implementation of healthcare AI solutions coupled with flourishing healthcare infrastructure of India and China are fostering the expansion of the regional market.

Global Artificial Intelligence in Diagnostics Market Component Type Sub-segments (Revenue, USD Million, 2017-2027)

Services
Hardware
Software

Global Artificial Intelligence in Diagnostics Market Diagnosis Type Sub-segments (Revenue, USD Million, 2017-2027)

Neurology
Chest & lung
Radiology
Pathology
Oncology
Cardiology
Others

Global Artificial Intelligence in Diagnostics Market Regional Analysis (Revenue, USD Million, 2017-2027)

Latin America

Mexico
Brazil

Asia-Pacific

South Korea
Australia
Japan
India
China
Rest of APAC

Europe

Italy
Spain
France
Germany
United Kingdom
Rest of Europe

North America

Canada
U.S.

Global Artificial Intelligence in Diagnostics Market Competitive Landscape (Revenue, USD Million, 2017-2027)

Zebra Medical Vision Ltd
Riverain Technologies
Siemens Healthineers AG
Neural Analytics, Inc.
IDx Technologies Inc.
VUNO Inc
Imagen Technologies, Inc.
GE Healthcare
AliveCor, Inc.
Aidoc Medical Ltd.

Table of Content:

Chapter 1. Executive Summary

1.1. Market Snapshot

1.2. Global & Segmental Market Estimates & Forecasts, 2018-2027 (USD Million)

1.2.1. Artificial Intelligence in Diagnostics Market, by Region, 2018-2027 (USD Million)

1.2.2. Artificial Intelligence in Diagnostics Market, by Component, 2018-2027 (USD Million)

1.2.3. Artificial Intelligence in Diagnostics Market, by Diagnosis Type, 2018-2027 (USD Million)

1.3. Key Trends

1.4. Estimation Methodology

1.5. Research Assumption

Chapter 2. Global Artificial Intelligence in Diagnostics Market Definition and Scope

2.1. Objective of the Study

2.2. Market Definition & Scope

2.2.1. Scope of the Study

2.2.2. Industry Evolution

2.3. Years Considered for the Study

2.4. Currency Conversion Rates

Chapter 3. Global Artificial Intelligence in Diagnostics Market Dynamics

3.1. Artificial Intelligence in Diagnostics Market Impact Analysis (2018-2027)

3.1.1. Market Drivers

3.1.2. Market Challenges

3.1.3. Market Opportunities

Chapter 4. Global Artificial Intelligence in Diagnostics Market Industry Analysis

4.1. Porter’s 5 Force Model

4.2. PEST Analysis

4.2.1. Political

4.2.2. Economical

4.2.3. Social

4.2.4. Technological

4.3. Investment Adoption Model

4.4. Analyst Recommendation & Conclusion

Chapter 5. Global Artificial Intelligence in Diagnostics Market, by Component

5.1. Market Snapshot

5.2. Global Artificial Intelligence in Diagnostics Market by Component, Performance – Potential Analysis

5.3. Global Artificial Intelligence in Diagnostics Market Estimates & Forecasts by Component 2017-2027 (USD Million)

5.4. Artificial Intelligence in Diagnostics Market, Sub Segment Analysis

5.4.1. Software

5.4.2. Hardware

5.4.3. Services

Chapter 6. Global Artificial Intelligence in Diagnostics Market, by Diagnosis Type

6.1. Market Snapshot

6.2. Global Artificial Intelligence in Diagnostics Market by Diagnosis Type, Performance – Potential Analysis

6.3. Global Artificial Intelligence in Diagnostics Market Estimates & Forecasts by Diagnosis Type 2017-2027 (USD Million)

6.4. Artificial Intelligence in Diagnostics Market, Sub Segment Analysis

6.4.1. Cardiology

6.4.2. Oncology

6.4.3. Pathology

6.4.4. Radiology

6.4.5. Chest and Lung

6.4.6. Neurology

6.4.7. Others

Chapter 7. Global Artificial Intelligence in Diagnostics Market, Regional Analysis

Related Report:

Molecular Diagnostics Market Size, Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2020 – 2026

Molecular Diagnostics Market size to grow at 9% CAGR from 2020 to 2026, as per new research report. The molecular diagnostics is a collective term for techniques used to analyze biological markers involved in a wide range of human ailments. Molecular diagnostic tests are the elementary part of a successful health care system. Molecular diagnostic tests provide critical information that helps the healthcare providers and patients to make the right medical decisions for better outcome of a medical procedure. The term molecular diagnostics is a class of diagnostic tests that evaluate health of a person literally at a molecular level, measuring and detecting specific genetic sequences in RNA or DNA or the proteins they express.

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Published at Tue, 01 Dec 2020 09:56:15 +0000

Opening the ‘black box’ of artificial intelligence

In February of 2013, Eric Loomis was driving around in the small town of La Crosse in Wisconsin, US, when he was stopped by the police. The car he was driving turned out to have been involved in a shooting, and he was arrested. Eventually a court sentenced him to six years in prison.

This might have been an uneventful case, had it not been for a piece of technology that had aided the judge in making the decision. They used COMPAS, an algorithm that determines the risk of a defendant becoming a recidivist. The court inputs a range of data, like the defendant’s demographic information, into the system, which yields a score of how likely they are to again commit a crime.

How the algorithm predicts this, however, remains non-transparent. The system, in other words, is a black box – a practice against which Loomis made a 2017 complaint in the US Supreme Court. He claimed COMPAS used gender and racial data to make its decisions, and ranked Afro-Americans as higher recidivism risks. The court eventually rejected his case, claiming the sentence would have been the same even without the algorithm. Yet there have also been a number of revelations which suggest COMPAS doesn’t accurately predict recidivism.

Adoption

While algorithmic sentencing systems are already in use in the US, in Europe their adoption has generally been limited. A Dutch AI sentencing system, that judged on private cases like late payments to companies, was for example shut down in 2018 after critical media coverage. Yet AI has entered into other fields across Europe. It is being rolled out to help European doctors diagnose Covid-19. And start-ups like the British M:QUBE, which uses AI to analyse mortgage applications, are popping up fast.

These systems run historical data through an algorithm, which then comes up with a prediction or course of action. Yet often we don’t know how such a system reaches its conclusion. It might work correctly, or it might have a technical error inside of it. It might even reproduce some form of bias, like racism, without the designers even realising it.

This is why researchers want to open this black box, and make AI systems transparent, or ‘explainable’, a movement that is now picking up steam. The EU White Paper on Artificial Intelligence released earlier this year called for explainable AI, major companies like Google and IBM are funding research into it and GDPR even includes a right to explainability for consumers.

‘We are now able to produce AI models that are very efficient in making decisions,’ said Fosca Giannotti, senior researcher at the Information Science and Technology Institute of the National Research Council in Pisa, Italy. ‘But often these models are impossible to understand for the end-user, which is why explainable AI is becoming so popular.’

Diagnosis

Giannotti leads a research project on explainable AI, called XAI, which wants to make AI systems reveal their internal logic. The project works on automated decision support systems like technology that helps a doctor make a diagnosis or algorithms that recommend to banks whether or not to give someone a loan. They hope to develop the technical methods or even new algorithms that can help make AI explainable.

‘Humans still make the final decisions in these systems,’ said Giannotti. ‘But every human that uses these systems should have a clear understanding of the logic behind the suggestion. ’

Today, hospitals and doctors increasingly experiment with AI systems to support their decisions, but are often unaware of how the decision was made. AI in this case analyses large amounts of medical data, and yields a percentage of likelihood a patient has a certain disease.

For example, a system might be trained on large amounts of photos of human skin, which in some cases represent symptoms of skin cancer. Based on that data, it predicts whether someone is likely to have skin cancer from new pictures of a skin anomaly. These systems are not general practice yet, but hospitals are increasingly testing them, and integrating them in their daily work.

These systems often use a popular AI method called deep learning, that takes large amounts of small sub-decisions. These are grouped into a network with layers that can range from a few dozen up to hundreds deep, making it particularly hard to see why the system suggested someone has skin cancer, for example, or to identify faulty reasoning.

‘Sometimes even the computer scientist who designed the network cannot really understand the logic,’ said Giannotti.

Natural language

For Senén Barro, professor of computer science and artificial intelligence at the University of Santiago de Compostela in Spain, AI should not only be able to justify its decisions but do so using human language.

‘Explainable AI should be able to communicate the outcome naturally to humans, but also the reasoning process that justifies the result,’ said Prof. Barro.

He is scientific coordinator of a project called NL4XAI which is training researchers on how to make AI systems explainable, by exploring different sub-areas such as specific techniques to accomplish explainability.

He says that the end result could look similar to a chatbot. ‘Natural language technology can build conversational agents that convey these interactive explanations to humans,’ he said.

‘Explainable AI should be able to communicate the outcome naturally to humans, but also the reasoning process that justifies the result.’

Prof. Senén Barro, University of Santiago de Compostela Spain

Another method to give explanations is for the system to provide a counterfactual. ‘It might mean that the system gives an example of what someone would need to change to alter the solution,’ said Giannotti. In the case of a loan-judging algorithm, a counterfactual might show to someone whose loan was denied what the nearest case would be where they would be approved. It might say that someone’s salary is too low, but if they earned €1,000 more on a yearly basis, they would be eligible.

White box

Giannotti says there are two main approaches to explainability. One is to start from black box algorithms, which are not capable of explaining their results themselves, and find ways to uncover their inner logic. Researchers can attach another algorithm to this black box system – an ‘explanator’ – which asks a range of questions of the black box and compares the results with the input it offered. From this process the explanator can reconstruct how the black box system works.

‘But another way is just to throw away the black box, and use white box algorithms, ’ said Giannotti. These are machine learning systems that are explainable by design, yet often are less powerful than their black box counterparts.

‘We cannot yet say which approach is better,’ cautioned Giannotti. ‘The choice depends on the data we are working on.’ When analysing very big amounts of data, like a database filled with high-resolution images, a black box system is often needed because they are more powerful. But for lighter tasks, a white box algorithm might work better.

Finding the right approach to achieving explainability is still a big problem though. Researchers need to find technical measures to see whether an explanation actually explains a black-box system well. ‘The biggest challenge is on defining new evaluation protocols to validate the goodness and effectiveness of the generated explanation,’ said Prof. Barro of NL4XAI.

On top of that, the exact definition of explainability is somewhat unclear, and depends on the situation in which it is applied. An AI researcher who writes an algorithm will need a different kind of explanation compared to a doctor who uses a system to make medical diagnoses.

‘Human evaluation (of the system’s output) is inherently subjective since it depends on the background of the person who interacts with the intelligent machine,’ said Dr Jose María Alonso, deputy coordinator of NL4XAI and also a researcher at the University of Santiago de Compostela.

Yet the drive for explainable AI is moving along step by step, which would improve cooperation between humans and machines. ‘Humans won’t be replaced by AI,’ said Giannotti. ‘They will be amplified by computers. But explanation is an important precondition for this cooperation.’

The research in this article was funded by the EU. If you liked this article, please consider sharing it on social media.

Published at Tue, 01 Dec 2020 09:42:31 +0000