How automation could turn doctors into Uber drivers — and how to stop it - ABC Religion & Ethics

How automation could turn doctors into Uber drivers — and how to stop it – ABC Religion & Ethics

The Jobs and Skills Summit being convened this week in Canberra sets out to address the employment challenges that Australia currently faces. But even as we respond to today’s skills shortage, we must also keep an eye on the long-term consequences of Artificial Intelligence (AI) and machine learning on jobs.

Fantasies in which AIs advance to the point of doing all of our jobs are seductive in some tech circles. Elon Musk has long promised fully driverless cars that will be safer than cars driven by humans. When these vehicles do arrive, they may delete the human Uber driver. But Musk’s repeatedly failed forecasts of fully driverless cars “next year” suggest that we will have human Uber drivers for a while yet.

Digital technologies are dislodging workers from some areas of the economy. But many jobs remain and new jobs are being created. Rather than abolishing human workers, digital technologies instead fundamentally change the nature of work in the industries they enter. Hence new jobs exist in the shadow of AI. According to the Australian Treasury, “Increased digitalisation … will change the nature of work and increase demand for workers with high levels of digital and data literacy.” Some of these new jobs will be thrilling. Imagine getting to work on the social media tech that could replace TikTok.

But as digitalisation enters an industry, many new jobs will fall into the category of digital adjunct worker. If you want to know what it is like to be a digital adjunct worker, think of the many people who find themselves fielding the complaints of telco customers during an economic downturn. They face angry calls from people who’ve endured long wait times only to be told that it’s not clear why they have failed to receive a contracted service.

Here I want to consider the prospects of digital adjunct workers in healthcare, an area of focus as we exit the pandemic. Because these domains are partially automated, careers in medicine move far from what most hope for from a career in healthcare. Aspiring healthcare workers may find that their work experience is rather more like that of the digital adjunct workers at an Amazon fulfilment centre than Dr Quinn, Medicine Woman or Marcus Welby, MD. They’ll be doing nothing more or less than what the machine directs. They may be paid accordingly.

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What will it be like to be a doctor in a future digital economy?

In their 2017 book Machine, Platform, Crowd: Harnessing Our Digital Future, the MIT economists Andrew McAfee and Erik Brynjolfsson extoll potential contributions to medicine of machine learning. “If the world’s best diagnostician in most specialties — radiology, pathology, oncology, and so on — is not already digital — it soon will be.” This needn’t be bad news for doctors. “Most patients … don’t want to get their diagnosis from a machine.” So we’ll still need human doctors.

But suppose we pose the question in crude economic terms. Much of the reason doctors are paid so well is that they have acquired the knowledge of many years of medical school and subsequent experience doctoring. Now think of the future as described by McAfee and Brynjolfsson. Machine learners have entered medicine and are now offering diagnoses and treatments for cancer based on the vast totality of published research and data on the disease. You might say that it surely can’t hurt to have an experienced human doctor look over the machine’s diagnoses and recommendations. Digital diagnosticians won’t be perfect. They are, after all, techs made by fallible humans. When they do get it wrong it will make news in much the way that fatalities from driverless cars make news today.

But the right way to ask the question is comparative. How often will a future digital diagnostician err when compared with a human? Advances in autopilots have seen pilots relegated to aviation digital adjuncts. Nicholas Carr’s 2014 book The Glass Cage makes clear that we are now seeing crashes that happen precisely because pilots mistakenly intervene to correct what seems to them to be an autopilot error but isn’t.

Suppose a medical tech of the future recommends that you try chemotherapy A rather than chemotherapy B. Should you instead go with your doctor who enjoyed a particularly charismatic presentation by a pharmaceutical rep selling B? Will you want a doctor who arrogates a right to countermand the directive a machine learner that draws on the totality of all clinical trials on your condition? Might it be better to have a medical digital adjunct who wouldn’t dare offer an opinion about how your disease should be treated? To pose the question in economic terms: do you need to pay that digital adjunct more than the many other digital gig workers? This is how a hospital administrator having to pay for increasingly expensive medical tech might ask the question.

When might this happen? Musk’s repeated mispredictions of the arrival of fully driverless cars show that we can get carried away by the hype in digital tech. Anyone who forecasts a flawless digital oncologist next year is bluffing. But it would be reckless for the Jobs and Skills Summit not to be alert to its possibility and to prepare for it.

A future social economy?

If machine learners do enter your industry, you might prepare by serenely accepting your status as poorly remunerated digital adjunct worker. But there is another response in which workers are paid for being human. The expansion of the digital economy could see a matching social economy whose workers meet the needs of obligatorily gregarious humans.

Is this possible? Some of us are already well paid for being human even when our jobs could be tolerably automated. One of the thrills of 2022 was seeing the preternaturally young Tom Cruise in Top Gun: Maverick. Advances in CGI are now providing characters that are difficult to distinguish from humans. In the movies we insist on the real Tom, not an animation guaranteed to look as youthful as the Cruise of the 1986 Top Gun. We pay him accordingly.

If we create a social economy, we could extend this privilege to many workers who increasingly work with machines. We should extend this human privilege both to workers who use digital tech to diagnose cancer and to make coffee.

Nicholas Agar is Professor of Philosophy at the University of Waikato in Aotearoa New Zealand, and the author of How to be Human in the Digital Economy. His book “Dialogues on Human Enhancement” is forthcoming with Routledge.

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Published at Tue, 30 Aug 2022 11:54:44 +0000

Artificial Intelligence-powered (AI) Spatial Biology Market Market to Record an Exponential …

JERSEY CITY, N.J., Aug. 30, 2022 /PRNewswire/ — InsightAce Analytic Pvt. Ltd. announces the release of market assessment report on “Global Artificial Intelligence-powered (AI) Spatial Biology Market By Data Analyzed (DNA, RNA, and Protein) By Application (Translation Research, Drug Discovery and Development, Single Cell Analysis, Cell Biology, Clinical Diagnostics, and Other Applications)– Technology Trends, Industry Competition Analysis, Revenue and Forecast Till 2030″

According to the latest research by InsightAce Analytic, the global artificial intelligence-powered (AI) spatial biology market is expected to record a promising CAGR of 16.4% during the period of 2022-2030. By region, North America dominates the global market with the major contribution in terms of revenue.

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In recent years, enormous advances in biological research and automated molecular biology have been gained using artificial intelligence (AI). AI has the ability to effectively assist in specific areas in biology, which may enable novel biotechnology-derived medicines to facilitate the deployment of precision medicine approaches. It is predicted that using AI on cell-by-cell maps of gene or protein activity will lead to major inventions in spatial biology. The next significant step in the comprehension of biology might be achieved by incorporating spatially resolved data. When applied to gene expression, spatial transcriptomics (spRNA-Seq) combines the strengths of conventional histopathology with those of single-cell gene expression profiling. Mapping specific disease pathologies is made possible by linking the spatial arrangement of molecules in cells and tissues with their gene expression state. Machine learning has the ability to generate images of gene transcripts at sub-cellular resolution and decipher molecular proximities from sequencing data.

Artificial Intelligence in spatial biology has gained faster development in sequencing and analysis, drug discovery, and disease diagnosis. Increased interest in AI in spatial biology can be attributed to the widespread use of similar technologies in other sectors and the growing popularity of increased use of Artificial Intelligence. Moreover, Market expansion can also be attributed to government spending on research around the world. The increasing demand for novel analysis analytical tools and subsequent funding has resulted in the market launch of high-throughput technology. However, Despite the availability of new high-complexity spatial imaging methods, it is still challenging and labour-intensive to extract, analyze, and interpret biological information from these images.

In 2021, the market was led by North America. Technological developments, the existence of a well-established research infrastructure and key players, and increased spending in drug discovery R&D are all factors contributing to the expansion of the regional market. Due to the region’s large and growing demand from research and the pharmaceutical industry, North America is currently the largest market for artificial intelligence applications in spatial omics.

The major players operating in artificial intelligence-powered (AI) spatial biology market players are Nucleai, Inc., Reveal Biosciences, Inc., Alpenglow Biosciences, SpIntellx, Inc., ONCOHOST, Pathr.ai, Phenomic AI, BioTuring Inc., Indica Labs, Rebus Biosystems, Inc., Genoskin, Algorithmic Biologics, Castle Biosciences, Inc. (TissueCypher), and Other Prominent Players. The leading spatial omics solution providers are focusing on strategies like investmenst for innovations, partnerships, collaborations, mergers, and agreements with AI based service providers.

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Key Developments In The Market

In Aug 2022, SpIntellx, Inc. and iCura Diagnostics announced cooperation to revolutionize precision oncology by releasing the power of genomic, proteomic, and transcriptomic data through the application of advanced spatial analytics. This new alliance combines SpIntellx’s software-as-a-service (SaaS) solutions for precision pathology applications that leverage unbiased spatial analytics and explainable AI with iCura Diagnostics’ technical CRO expertise in accelerating immunotherapy and targeted therapy development.
In July 2022, Nucleai announced a relationship with Sirona DX, a US-based contract research company. The alliance intends to further the AI-driven identification of novel spatial biomarkers — indications of solid tumour recurrence, treatment response, and prognosis. Nucleai is developing a precision oncology platform based on artificial intelligence for research and therapy decisions.
In May 2022, OncoHost announced the completion of a Series C investment round worth $35 million. The financing will be used to expand OncoHost’s ongoing multicenter PROPHETIC trial utilizing PROphet®, the company’s machine learning-based host response profiling technology, and to support the upcoming commercial launch of the precision oncology diagnostic solution in the United States.
In March 2022, CellCarta announced the release of imageDx PRISM to significantly improve the spatial biology data that can be obtained from Akoya Bioscience’s® multiplex immunofluorescence (mIF) tests. imageDx PRISM from Reveal Biosciences integrates the most recent AI advancements to novel pattern discovery and spatial biomarker characterization, providing difficult-to-discover patient insights previously.
In Jan 2022, Single Cell Discoveries and BioTuring announced a partnership to advance the field of single-cell sequencing. Single Cell Discoveries will integrate BioTuring’s single-cell data processing solution into its single-cell sequencing services as part of the agreement. The cooperation intends to bridge the gap between wet-lab services for single-cell sequencing and solutions for single-cell data analysis.

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Market Segments

Global Artificial Intelligence-powered (AI) Spatial Biology Market, by Data Analyzed, 2022-2030 (Value US$ Mn)

DNA
RNA
Protein

Global Artificial Intelligence-powered (AI) Spatial Biology Market, by Application, 2022-2030 (Value US$ Mn)

Global Artificial Intelligence-powered (AI) Spatial Biology Market, by Region, 2022-2030 (Value US$ Mn)

North America
Europe
Asia Pacific
Latin America
Middle East & Africa

North America Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

U.S.
Canada

Europe Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

Germany
France
Italy
Spain
Russia
Rest of Europe

Asia Pacific Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

India
China
Japan
South Korea
Australia & New Zealand

Latin America Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

Brazil
Mexico
Rest of Latin America

Middle East & Africa Artificial Intelligence-powered (AI) Spatial Biology Market, by Country, 2022-2030 (Value US$ Mn)

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To receive industry overview and future trends of global artificial intelligence-powered (AI) spatial biology market
To analyse the artificial intelligence-powered (AI) spatial biology market drivers and challenges
To get information on artificial intelligence-powered (AI) spatial biology market size value (US$ Mn) forecast till 2030
To get information on major Investments, Mergers & Acquisition in global artificial intelligence-powered (AI) spatial biology market industry

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Other Related Reports Published by InsightAce Analytic:

Global Spatial Omics Solutions Market

Global Proteome Profiling Services Market

Global Single-Cell Bioinformatics Software and Services Market

Global Oligonucleotide Synthesis, Modification, and Purification Services Market

Global Circulating Cell-Free DNA (ccfDNA) Diagnostics Market

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