Improving the speed and accuracy of clinical diagnosis, augmenting clinical decision-making, reducing human error in clinical care, individualizing therapies based on a patient’s genomic and metabolomic profiles, differentiating benign from cancerous lesions with impeccable accuracy, identifying likely conditions a person may develop years down the road, spotting early tell-tale signs of an ultrarare disease, intercepting dangerous drug interactions before a patient is given a new medication, yielding real-time insights amidst a raging pandemic to inform optimal treatment of patients infected with a novel human pathogen.

These are some of the promises that physicians and researchers look to fulfill using artificial intelligence—promises poised to transform clinical care, lead to better patient outcomes and, ultimately, improve human lives.

Done right, artificial intelligence holds could achieve all this.

Yet, AI is no silver bullet. It can fall prey to the cognitive fallibilities and blind spots of the humans who design it. AI models can be as imperfect as the data and clinical practices that the machine-learning algorithms are trained on, propagating the very same biases AI was designed to eliminate in the first place.

Beyond conceptual and design pitfalls, realizing the potential of AI also requires overcoming systemic hurdles that stand in the way of integrating AI-based technologies into clinical practice.

How does the field of medicine move forward to harness the promise of AI? How does it eliminate the perils posed by its suboptimal design or inappropriate use? How can AI be integrated seamlessly into frontline clinical care?

These are some of the overarching questions that will be tackled at the inaugural Symposium on Artificial Intelligence for Learning Health Systems (SAIL) to be held in Hamilton, Bermuda, an event envisioned to become an annual conference.

Conceived by Zak Kohane, chair of DBMI, the symposium will bring together the brightest minds from academia and industry in the fields of computer science, AI, clinical medicine, and healthcare. The charge is to establish both the philosophical and practical frameworks toward optimal translation of AI to the clinic.

“How can we bring the best aspects of AI to augment the best and most human components of the patient-clinician relationship and to safely accelerate 21^st century medicine? These are the central questions that I hope we will be able to answer,” Kohane said.

One of the more pedestrian—and more immediate—goals of the symposium, however, will be to bridge the chasms between various players in AI and medicine by simply having them talk with each other.

“We have various communities involved in AI all expecting AI to somehow improve medicine, but they have no way of communicating and engaging with one another. So one of the goals of this symposium is to create communication—not around theoretical issue of methodology but around the pragmatics of implementation,” Kohane said.

But the idea is not merely to have theoreticians and methodologists engage with practitioners. It is to create a common space for conversation—and eventual collaboration—among experts who have traditionally worked in parallel rather than at intersections, including clinical informaticians, machine learning specialists, clinicians, administrators, medical journal editors, and those charged with implementing AI in health care.

The event was originally scheduled to take place in 2020, but the COVID-19 pandemic derailed those plans.

To keep the momentum going and to lay the groundwork for the main event, Kohane held a virtual warm-up session in the fall of 2020, during which experts mapped some of the most acute challenges and greatest opportunities in the field of medical AI.

Read full article in HMS News