Along with the benefits come new risks and regulatory challenges meaning doctors now have to consider how best to reduce risk when it comes to medical technology. From spotting malignant tumours to reading CT scans and mammograms, Artificial Intelligence and Machine Learning-based technology is faster and more accurate than traditional devices – or even the best doctors. In a new article, Algorithms on regulatory lockdown in medicine, recently published in Science, researchers look at the new challenges facing regulators as they navigate the unfamiliar pathways of Artificial Intelligence and Machine Learning. In the paper, the researchers consider the questions: what new risks do we face as Artificial Intelligence and Machine Learning (AI/ML) devices are developed and implemented? How should they be managed?

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices – or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices--or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article, "Algorithms on regulatory lockdown in medicine" recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center, look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?