Adopting AI in Health Care Will Be Slow and Difficult – Harvard Business Review

Executive Summary

Artificial intelligence, including machine learning, presents exciting opportunities totransformthe health and life sciences spaces. It offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers andpotential investorsto grapple with how to overcome todays barriers to adoption, compliance, and implementation. This article explains thekey obstacles and offers ways to overcome them.

Artificial intelligence, including machine learning, presents exciting opportunities totransformthe health and life sciences spaces. It offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers andpotential investorsto grapple with how to overcome todays barriers to adoption, compliance, and implementation.

Here are key obstacles to consider and how to handle them:

Developing regulatory frameworks. Over the past few years, the U.S. Food and Drug Administration (FDA) has been taking incremental steps toupdate its regulatory framework to keep up with the rapidly advancing digital health market. In 2017, the FDA released its Digital Health Innovation Action Plan to offer clarity about the agencys role in advancing safe and effective digital health technologies, and addressing key provisions of the 21st Century Cures Act.

The FDA has also been enrolling select software-as-a-medical-device (SaMD) developers in its Digital Health Software Precertification (Pre-Cert) Pilot Program. The goal of the Pre-Cert pilot is to help the FDA determine the key metrics and performance indicators required for product precertification, while also identifying ways to make the approval process easier for developers and help advance healthcare innovation.

Most recently, the FDA released in September its Policy for Device Software Functions and Mobile Medical Applications a series of guidance documents that describe how the agency plans to regulate software that aids in clinical decision support (CDS), including software that utilizes machine-learning-based algorithms.

In a related statement from the FDA, Amy Abernethy, its principal deputy commissioner, explained that the agency plans to focus regulatory oversight on higher-risk software functions, such as those used for more serious or critical health circumstances. This also includes software that utilizes machine learning-based algorithms, where users might not readily understand the programs logic and inputs without further explanation.

An example of CDS software that would fall under the FDAs higher-risk oversight category would be one that identifies a patient at risk for a potentially serious medical condition such as a postoperative cardiovascular eventbut does not explain why the software made that identification.

Achieving FDA approval. To account for the shifting FDA oversight and approval processes, software developers must carefully think through how to best design and roll out their product so its well positioned for FDA approval, especially if the software falls under the agencys higher risk category.

One factor that must be considered is the fact that AI-powered therapeutic or diagnostic tools, by nature, will continue to evolve. For example, it is reasonable to expect that a software product will be updated and change over time (e.g., security updates, adding new features or functionalities, updating an algorithm, etc.). But given the product has technically changed, its FDA approval status could be put at risk after each update or new iteration.

In this case, planning to take a version-based approach to the FDA approval process might be in the developers best interest. In this approach, a new version of software is created each time the softwares internal ML algorithm(s) is trained by a new set of data, with each new version being subjected to independent FDA approval.

Although cumbersome, this approach sidesteps FDA concerns about approving software products that functionally change post-FDA approval. These strategic development considerations are crucial for solutions providers to consider.

Similarly, investors must also have a clear understanding of a companys product development plans and intended approach for continual FDA approval as this can provide clear differentiation over other competitors in the same space. Clinicians will be hard pressed to adopt technologies that havent been validated by the FDA, so investors need to be sure the companies they are considering supporting have a clear product development roadmap including an approach to FDA approvals as software products themselves and regulatory guidelines continue to develop.

AI is a black box. Besides current regulatory ambiguity, another key issue that poses challenges to the adoption of AI applications in the clinical setting is theirblack-box nature and the resulting trust issues.

One challenge is tracking: If a negative outcome occurs, can an AI applications decision-making process be tracked and assessed for example, can users identify the training data and/or machine learning (ML) paradigm that led to the AI applications specific action?. To put it more simply, can the root cause of the negative outcome be identified within the technology so that it can be prevented in the future?

From reclassifying the training data to redesigning the ML algorithms that learn from the training data, the discovery process is complex and could even result in the application being removed from the marketplace.

Another concern raised about the black-box aspect of AI systems is that someone, either on purpose or by mistake, could feed incorrect data into the system, causing erroneous conclusions (e.g., misdiagnosis, incorrect treatment recommendations). Luckily, detection algorithms designed to identify doctored or incorrect inputs could reduce, if not eliminate, this risk.

A bigger challenge posed by AI systems black box nature is that physicians are reluctant to trust (due in part to malpractice-liability risk) and therefore adopt something that they dont fully understand. For example, there are a number of emerging AI imaging diagnostic companies with FDA-approved AI software tools that can assist clinicians in diagnosing and treating conditions such as strokes, diabetic retinopathy, intracranial hemorrhaging, and cancer.

However, clinical adoption of these AI tools has been slow. One reason is physician certification bodies such as the American College of Radiology (ACR) have only recently started releasing formalized use cases for how AI software tools can be reliably used. Patients are also likely to have trust issues with AI-powered technologies. While they may accept the reality that human errors can occur, they have very little tolerance of machine error.

While efforts to help open up the black box are underway, AIs most useful role in the clinical setting during this early period of adoption may be to help providers make better decisions rather than replacing them in the decision-making process. Most physicians may not trust a black box, but they will use it as a support system if they remain the final arbiter.

To gain physicians trust, AI-software developers will have to clearly demonstrate that when the solutions are integrated into the clinical decision-making process, they help the clinical team do a better job. The tools must also be simple and easy to use. Applying AI in lower-stakes tasks initially, such as billing and coding (e.g., diagnostics, AI-assisted treatments), should also help increase trust over time.

At the industry level, there needs to be a concerted effort to publish more formalized use cases that support AIs benefits. Software developers and investors should be working with professional associations such as the ACR to publish more use cases and develop more frameworks to spur industry adoption and get more credibility.

Lower hurdles in life sciences. While AIs application in the clinical care setting still faces many challenges, the barriers to adoption are lower for specific life sciences use cases. For instance,ML is an exceptional toolfor matching patients to clinical trials and for drug discovery and identifying effective therapies.

But whether its in a life sciences capacity or the clinical care setting, the fact remains that many stakeholders stand to be impacted by AIs proliferation in health care and life sciences. Obstacles certainly exist for AIs wider adoption from regulatory uncertainties to the lack of trust to the dearth of validated use cases. But the opportunities the technology presents to change the standard of care, improve efficiencies, and help clinicians make more informed decisions is worth the effort to overcome them.

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Adopting AI in Health Care Will Be Slow and Difficult - Harvard Business Review