The future of healthcare: MIT, Bayer and others leverage AI, machine learning

The future of healthcare: MIT, Bayer and others use AI, machine learning

From search engines to social media, algorithms are quickly becoming a part of everyday life, and businesses and academics love Bayerthe Massachusetts Institute of Technology (MIT) and others use them to their advantage.

In artificial intelligence (AI) and machine learning (ML), algorithms are used to solve complex problems, including in preventive healthcare, diagnostics and drug discovery. Read on to find out how these technologies are being used in the life sciences.

Bayer improves radiological diagnostics with AI

One of the most time-consuming diagnostic tools available is radiology. First, the patient sits tight for the required time frame, before a radiologist sits down to analyze the captured images. All sorts of problems can arise during this lengthy process, including misdiagnosis or overlooking a crucial, small detail in a scan. Industry giant Bayer saw this problem and set to work to find a solution that could shorten radiology timescales while increasing accuracy.

Bayer has introduced Calantic Digital Solutions, a cloud-based technology designed to improve scheduling and enhance radiologic diagnostics using AI. The scheduling is optimized as patients are prioritized based on need and treated accordingly.

The technology can be used for computed tomography (CT), X-rays, or even magnetic resonance imaging (MRI). A article published in PharmaPhorum regarding Bayer’s new approach points to a study conducted in 2018 reporting up to 40 million diagnostic errors annually, all attributed to medical imaging.

Gerd Krueger, head of radiology at Bayer, explained why the company wants to bring the new technology to market.

“With Calantic Digital Solutions, we are entering the fastest growing segment in the radiology market and taking the next step from a product provider to a solutions provider,” said Krueger.

MIT touts ML and AI and highlights ethical concerns

the famous Massachusetts Institute of Technology recognizes the changes taking place in biotechnology and healthcare, going so far as to identification piece that states, “It was exciting to see technology that rewrites and improves on what we thought was an established health concept.”

The article goes on to explain how AI and ML have infiltrated human life under the guise of electronic assistants like the FitBit or Siri, translating our speech into real purchases or calling 911 because dangerous vital signs have been recorded.

However, the MIT article gives a brief caveat between laudatory words. While these systems seem omniscient, ethics cannot be programmed. Concerns about AI and ML ethics question whether the technology will mimic the way some physicians overlook or misdiagnose conditions in underrepresented populations. Because AI and ML learn by instruction, this problem could follow medicine into the technological future.

MultiOmic Health and Mesh Bio use AI to fill research gaps

While some remain skeptical of AI’s ability to uniformly improve the lives of all patients, others are moving forward and addressing the concerns.

A study in Asia sponsored by MultiOmic Health and Mesh Bio is in the works, specifically focused on using AI to access and analyze the data of patients with chronic metabolic diseases.

Andrew Wu, Ph.D., co-founder and CEO of Mesh Bio, commented on the shared goal.

“We are excited to partner with MultiOmic Health on this important study for patients in Asia. Their therapeutic development programs for the intervention of metabolic diseases have deep synergies with Mesh Bio’s mission to develop digital care solutions for these diseases,” he said.

Asian populations have historically been underrepresented in medical literature and research, including in chronic metabolic diseases. The AI ​​technology will use samples of biological substances from patients to analyze genetic, proteomic and metabolic data, in addition to traditional clinical and/or diagnostic tests. The information collected can be used in future treatment research and development efforts.

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