During the Covid-19 pandemic, digital health technologies transformed the way that many of us received health care, supercharging the uptake of digital tools such as telehealth platforms, mobile symptom trackers, and remote monitoring. However, the whole-scale adoption and impact of digital health technology in national health systems around the world has still not yet fully materialized. A critical reason is that they often lack the necessary scientific evidence to back the range of benefits--from improved health outcomes for patients to better cost-benefit outcomes for payers such as insurance companies and health care providers--that its manufacturers claim they can deliver. A recent study by healthtech seed fund Rock Health and Johns Hopkins University demonstrated the extent of the problem. The researchers reviewed the associated clinical trials, regulatory claims filings, and listed outcomes by 224 healthcare companies.

As pressures on healthcare systems intensify, an increasing number of consumers are turning to the use of symptom checkers in the search for fast answers – or guidance – to any concerns that they may have. But although these tools have become popular, questions around their accuracy, and not only, are plaguing the digital health space. Last week, at Slush, Ada Health cofounder and chief medical officer Claire Novorol spoke to Wired UK's Victoria Turk about the Berlin-headquartered company's approach to building trust in its AI-powered chatbot. "Absolutely key, first of all, is the quality of the product," Novorol told the audience. "So what we've always focused on from the very beginning, eight years ago now actually, is the quality of the core product, the foundation of everything that we do, and that's our knowledge base, our reasoning engine, and how it works, the clinical quality of that, accuracy, safety."

Interest in artificial intelligence and machine learning in health care is accelerating and diversifying. The promise of AI in health care, though still somewhat speculative, is profound and envisions capabilities that were the stuff of science fiction just a few years ago. The challenge of AI in health care is equally profound and requires answering difficult questions and addressing thorny issues. What are the realistic promises that AI can make? How does AI intersect with other emerging health care capabilities, such as genomic medicine?

Baidu, a China-based search engine business, took the wraps off a digital health tool to field medical queries and conversations between physicians and their patients called Melody medical assistant. The company claimed in a news release that the app uses deep learning to help doctors gather information from patients about their medical conditions and help physicians arrive at a diagnosis. To give an idea how the bot is designed to work, a spokeswoman provided an overview, in response to emailed questions. When a patient opens the app to pose a question, Melody asks the patient relevant follow-up questions to clarify information such as the duration, severity, and frequency of symptoms. The questions can also touch on additional symptoms related to the condition, even though the patient may not have mentioned them. The point is to give the doctor a more detailed sense of the patient's condition to decide whether to recommend the patient for an appointment sooner rather than later.